research hypothesis and assumption

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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The Research Hypothesis: Role and Construction

• First Online: 01 January 2012

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• Phyllis G. Supino EdD 3  

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A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).

A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.

Wrong hypotheses, rightly worked from, have produced more results than unguided observation

—Augustus De Morgan, 1872[ 1 ]—

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The Nature and Logic of Science: Testing Hypotheses

Abductive Research Methods in Psychological Science

Abductive Research Methods in Psychological Science

De Morgan A, De Morgan S. A budget of paradoxes. London: Longmans Green; 1872.

Google Scholar  

Leedy Paul D. Practical research. Planning and design. 2nd ed. New York: Macmillan; 1960.

Bernard C. Introduction to the study of experimental medicine. New York: Dover; 1957.

Erren TC. The quest for questions—on the logical force of science. Med Hypotheses. 2004;62:635–40.

Article   PubMed   Google Scholar  

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 7. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1966.

Aristotle. The complete works of Aristotle: the revised Oxford Translation. In: Barnes J, editor. vol. 2. Princeton/New Jersey: Princeton University Press; 1984.

Polit D, Beck CT. Conceptualizing a study to generate evidence for nursing. In: Polit D, Beck CT, editors. Nursing research: generating and assessing evidence for nursing practice. 8th ed. Philadelphia: Wolters Kluwer/Lippincott Williams and Wilkins; 2008. Chapter 4.

Jenicek M, Hitchcock DL. Evidence-based practice. Logic and critical thinking in medicine. Chicago: AMA Press; 2005.

Bacon F. The novum organon or a true guide to the interpretation of nature. A new translation by the Rev G.W. Kitchin. Oxford: The University Press; 1855.

Popper KR. Objective knowledge: an evolutionary approach (revised edition). New York: Oxford University Press; 1979.

Morgan AJ, Parker S. Translational mini-review series on vaccines: the Edward Jenner Museum and the history of vaccination. Clin Exp Immunol. 2007;147:389–94.

Article   PubMed   CAS   Google Scholar  

Pead PJ. Benjamin Jesty: new light in the dawn of vaccination. Lancet. 2003;362:2104–9.

Lee JA. The scientific endeavor: a primer on scientific principles and practice. San Francisco: Addison-Wesley Longman; 2000.

Allchin D. Lawson’s shoehorn, or should the philosophy of science be rated, ‘X’? Science and Education. 2003;12:315–29.

Article   Google Scholar  

Lawson AE. What is the role of induction and deduction in reasoning and scientific inquiry? J Res Sci Teach. 2005;42:716–40.

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 2. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1965.

Bonfantini MA, Proni G. To guess or not to guess? In: Eco U, Sebeok T, editors. The sign of three: Dupin, Holmes, Peirce. Bloomington: Indiana University Press; 1983. Chapter 5.

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 5. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1965.

Flach PA, Kakas AC. Abductive and inductive reasoning: background issues. In: Flach PA, Kakas AC, ­editors. Abduction and induction. Essays on their relation and integration. The Netherlands: Klewer; 2000. Chapter 1.

Murray JF. Voltaire, Walpole and Pasteur: variations on the theme of discovery. Am J Respir Crit Care Med. 2005;172:423–6.

Danemark B, Ekstrom M, Jakobsen L, Karlsson JC. Methodological implications, generalization, scientific inference, models (Part II) In: explaining society. Critical realism in the social sciences. New York: Routledge; 2002.

Pasteur L. Inaugural lecture as professor and dean of the faculty of sciences. In: Peterson H, editor. A treasury of the world’s greatest speeches. Douai, France: University of Lille 7 Dec 1954.

Swineburne R. Simplicity as evidence for truth. Milwaukee: Marquette University Press; 1997.

Sakar S, editor. Logical empiricism at its peak: Schlick, Carnap and Neurath. New York: Garland; 1996.

Popper K. The logic of scientific discovery. New York: Basic Books; 1959. 1934, trans. 1959.

Caws P. The philosophy of science. Princeton: D. Van Nostrand Company; 1965.

Popper K. Conjectures and refutations. The growth of scientific knowledge. 4th ed. London: Routledge and Keegan Paul; 1972.

Feyerabend PK. Against method, outline of an anarchistic theory of knowledge. London, UK: Verso; 1978.

Smith PG. Popper: conjectures and refutations (Chapter IV). In: Theory and reality: an introduction to the philosophy of science. Chicago: University of Chicago Press; 2003.

Blystone RV, Blodgett K. WWW: the scientific method. CBE Life Sci Educ. 2006;5:7–11.

Kleinbaum DG, Kupper LL, Morgenstern H. Epidemiological research. Principles and quantitative methods. New York: Van Nostrand Reinhold; 1982.

Fortune AE, Reid WJ. Research in social work. 3rd ed. New York: Columbia University Press; 1999.

Kerlinger FN. Foundations of behavioral research. 1st ed. New York: Hold, Reinhart and Winston; 1970.

Hoskins CN, Mariano C. Research in nursing and health. Understanding and using quantitative and qualitative methods. New York: Springer; 2004.

Tuckman BW. Conducting educational research. New York: Harcourt, Brace, Jovanovich; 1972.

Wang C, Chiari PC, Weihrauch D, Krolikowski JG, Warltier DC, Kersten JR, Pratt Jr PF, Pagel PS. Gender-specificity of delayed preconditioning by isoflurane in rabbits: potential role of endothelial nitric oxide synthase. Anesth Analg. 2006;103:274–80.

Beyer ME, Slesak G, Nerz S, Kazmaier S, Hoffmeister HM. Effects of endothelin-1 and IRL 1620 on myocardial contractility and myocardial energy metabolism. J Cardiovasc Pharmacol. 1995;26(Suppl 3):S150–2.

PubMed   CAS   Google Scholar  

Stone J, Sharpe M. Amnesia for childhood in patients with unexplained neurological symptoms. J Neurol Neurosurg Psychiatry. 2002;72:416–7.

Naughton BJ, Moran M, Ghaly Y, Michalakes C. Computer tomography scanning and delirium in elder patients. Acad Emerg Med. 1997;4:1107–10.

Easterbrook PJ, Berlin JA, Gopalan R, Matthews DR. Publication bias in clinical research. Lancet. 1991;337:867–72.

Stern JM, Simes RJ. Publication bias: evidence of delayed publication in a cohort study of clinical research projects. BMJ. 1997;315:640–5.

Stevens SS. On the theory of scales and measurement. Science. 1946;103:677–80.

Knapp TR. Treating ordinal scales as interval scales: an attempt to resolve the controversy. Nurs Res. 1990;39:121–3.

The Cochrane Collaboration. Open Learning Material. www.cochrane-net.org/openlearning/html/mod14-3.htm . Accessed 12 Oct 2009.

MacCorquodale K, Meehl PE. On a distinction between hypothetical constructs and intervening ­variables. Psychol Rev. 1948;55:95–107.

Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: ­conceptual, strategic and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82.

Williamson GM, Schultz R. Activity restriction mediates the association between pain and depressed affect: a study of younger and older adult cancer patients. Psychol Aging. 1995;10:369–78.

Song M, Lee EO. Development of a functional capacity model for the elderly. Res Nurs Health. 1998;21:189–98.

MacKinnon DP. Introduction to statistical mediation analysis. New York: Routledge; 2008.

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Supino, P.G. (2012). The Research Hypothesis: Role and Construction. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_3

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How to Write a Research Hypothesis

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Since grade school, we've all been familiar with hypotheses. The hypothesis is an essential step of the scientific method. But what makes an effective research hypothesis, how do you create one, and what types of hypotheses are there? We answer these questions and more.

Updated on April 27, 2022

What is a research hypothesis?

General hypothesis.

Since grade school, we've all been familiar with the term “hypothesis.” A hypothesis is a fact-based guess or prediction that has not been proven. It is an essential step of the scientific method. The hypothesis of a study is a drive for experimentation to either prove the hypothesis or dispute it.

Research Hypothesis

A research hypothesis is more specific than a general hypothesis. It is an educated, expected prediction of the outcome of a study that is testable.

What makes an effective research hypothesis?

A good research hypothesis is a clear statement of the relationship between a dependent variable(s) and independent variable(s) relevant to the study that can be disproven.

Research hypothesis checklist

Once you've written a possible hypothesis, make sure it checks the following boxes:

• It must be testable: You need a means to prove your hypothesis. If you can't test it, it's not a hypothesis.
• It must include a dependent and independent variable: At least one independent variable ( cause ) and one dependent variable ( effect ) must be included.
• The language must be easy to understand: Be as clear and concise as possible. Nothing should be left to interpretation.
• It must be relevant to your research topic: You probably shouldn't be talking about cats and dogs if your research topic is outer space. Stay relevant to your topic.

How to create an effective research hypothesis

Pose it as a question first.

Start your research hypothesis from a journalistic approach. Ask one of the five W's: Who, what, when, where, or why.

A possible initial question could be: Why is the sky blue?

Do the preliminary research

Once you have a question in mind, read research around your topic. Collect research from academic journals.

If you're looking for information about the sky and why it is blue, research information about the atmosphere, weather, space, the sun, etc.

Write a draft hypothesis

Once you're comfortable with your subject and have preliminary knowledge, create a working hypothesis. Don't stress much over this. Your first hypothesis is not permanent. Look at it as a draft.

Your first draft of a hypothesis could be: Certain molecules in the Earth's atmosphere are responsive to the sky being the color blue.

Make your working draft perfect

Take your working hypothesis and make it perfect. Narrow it down to include only the information listed in the “Research hypothesis checklist” above.

Now that you've written your working hypothesis, narrow it down. Your new hypothesis could be: Light from the sun hitting oxygen molecules in the sky makes the color of the sky appear blue.

Write a null hypothesis

Your null hypothesis should be the opposite of your research hypothesis. It should be able to be disproven by your research.

In this example, your null hypothesis would be: Light from the sun hitting oxygen molecules in the sky does not make the color of the sky appear blue.

Why is it important to have a clear, testable hypothesis?

One of the main reasons a manuscript can be rejected from a journal is because of a weak hypothesis. “Poor hypothesis, study design, methodology, and improper use of statistics are other reasons for rejection of a manuscript,” says Dr. Ish Kumar Dhammi and Dr. Rehan-Ul-Haq in Indian Journal of Orthopaedics.

According to Dr. James M. Provenzale in American Journal of Roentgenology , “The clear declaration of a research question (or hypothesis) in the Introduction is critical for reviewers to understand the intent of the research study. It is best to clearly state the study goal in plain language (for example, “We set out to determine whether condition x produces condition y.”) An insufficient problem statement is one of the more common reasons for manuscript rejection.”

Characteristics that make a hypothesis weak include:

• Unclear variables
• Unoriginality
• Too general
• Too specific

A weak hypothesis leads to weak research and methods . The goal of a paper is to prove or disprove a hypothesis - or to prove or disprove a null hypothesis. If the hypothesis is not a dependent variable of what is being studied, the paper's methods should come into question.

A strong hypothesis is essential to the scientific method. A hypothesis states an assumed relationship between at least two variables and the experiment then proves or disproves that relationship with statistical significance. Without a proven and reproducible relationship, the paper feeds into the reproducibility crisis. Learn more about writing for reproducibility .

In a study published in The Journal of Obstetrics and Gynecology of India by Dr. Suvarna Satish Khadilkar, she reviewed 400 rejected manuscripts to see why they were rejected. Her studies revealed that poor methodology was a top reason for the submission having a final disposition of rejection.

Aside from publication chances, Dr. Gareth Dyke believes a clear hypothesis helps efficiency.

“Developing a clear and testable hypothesis for your research project means that you will not waste time, energy, and money with your work,” said Dyke. “Refining a hypothesis that is both meaningful, interesting, attainable, and testable is the goal of all effective research.”

Types of research hypotheses

There can be overlap in these types of hypotheses.

Simple hypothesis

A simple hypothesis is a hypothesis at its most basic form. It shows the relationship of one independent and one independent variable.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable).

Complex hypothesis

A complex hypothesis shows the relationship of two or more independent and dependent variables.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable) and heart disease (dependent variable).

Directional hypothesis

A directional hypothesis guesses which way the results of an experiment will go. It uses words like increase, decrease, higher, lower, positive, negative, more, or less. It is also frequently used in statistics.

Example: Humans exposed to radiation have a higher risk of cancer than humans not exposed to radiation.

Non-directional hypothesis

A non-directional hypothesis says there will be an effect on the dependent variable, but it does not say which direction.

Associative hypothesis

An associative hypothesis says that when one variable changes, so does the other variable.

Alternative hypothesis

An alternative hypothesis states that the variables have a relationship.

• The opposite of a null hypothesis

Example: An apple a day keeps the doctor away.

Null hypothesis

A null hypothesis states that there is no relationship between the two variables. It is posed as the opposite of what the alternative hypothesis states.

Researchers use a null hypothesis to work to be able to reject it. A null hypothesis:

• Can never be proven
• Can only be rejected
• Is the opposite of an alternative hypothesis

Example: An apple a day does not keep the doctor away.

Logical hypothesis

A logical hypothesis is a suggested explanation while using limited evidence.

Example: Bats can navigate in the dark better than tigers.

In this hypothesis, the researcher knows that tigers cannot see in the dark, and bats mostly live in darkness.

Empirical hypothesis

An empirical hypothesis is also called a “working hypothesis.” It uses the trial and error method and changes around the independent variables.

• An apple a day keeps the doctor away.
• Two apples a day keep the doctor away.
• Three apples a day keep the doctor away.

In this case, the research changes the hypothesis as the researcher learns more about his/her research.

Statistical hypothesis

A statistical hypothesis is a look of a part of a population or statistical model. This type of hypothesis is especially useful if you are making a statement about a large population. Instead of having to test the entire population of Illinois, you could just use a smaller sample of people who live there.

Example: 70% of people who live in Illinois are iron deficient.

Causal hypothesis

A causal hypothesis states that the independent variable will have an effect on the dependent variable.

Example: Using tobacco products causes cancer.

Final thoughts

Make sure your research is error-free before you send it to your preferred journal . Check our our English Editing services to avoid your chances of desk rejection.

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Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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How to write a research hypothesis

Last updated

19 January 2023

Reviewed by

Miroslav Damyanov

Start with a broad subject matter that excites you, so your curiosity will motivate your work. Conduct a literature search to determine the range of questions already addressed and spot any holes in the existing research.

Narrow the topics that interest you and determine your research question. Rather than focusing on a hole in the research, you might choose to challenge an existing assumption, a process called problematization. You may also find yourself with a short list of questions or related topics.

Use the FINER method to determine the single problem you'll address with your research. FINER stands for:

I nteresting

You need a feasible research question, meaning that there is a way to address the question. You should find it interesting, but so should a larger audience. Rather than repeating research that others have already conducted, your research hypothesis should test something novel or unique. 

The research must fall into accepted ethical parameters as defined by the government of your country and your university or college if you're an academic. You'll also need to come up with a relevant question since your research should provide a contribution to the existing research area.

This process typically narrows your shortlist down to a single problem you'd like to study and the variable you want to test. You're ready to write your hypothesis statements.

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• Types of research hypotheses

It is important to narrow your topic down to one idea before trying to write your research hypothesis. You'll only test one problem at a time. To do this, you'll write two hypotheses – a null hypothesis (H0) and an alternative hypothesis (Ha).

You'll come across many terms related to developing a research hypothesis or referring to a specific type of hypothesis. Let's take a quick look at these terms.

Null hypothesis

The term null hypothesis refers to a research hypothesis type that assumes no statistically significant relationship exists within a set of observations or data. It represents a claim that assumes that any observed relationship is due to chance. Represented as H0, the null represents the conjecture of the research.

Alternative hypothesis

The alternative hypothesis accompanies the null hypothesis. It states that the situation presented in the null hypothesis is false or untrue, and claims an observed effect in your test. This is typically denoted by Ha or H(n), where “n” stands for the number of alternative hypotheses. You can have more than one alternative hypothesis. 

Simple hypothesis

The term simple hypothesis refers to a hypothesis or theory that predicts the relationship between two variables - the independent (predictor) and the dependent (predicted). 

Complex hypothesis

The term complex hypothesis refers to a model – either quantitative (mathematical) or qualitative . A complex hypothesis states the surmised relationship between two or more potentially related variables.

Directional hypothesis

When creating a statistical hypothesis, the directional hypothesis (the null hypothesis) states an assumption regarding one parameter of a population. Some academics call this the “one-sided” hypothesis. The alternative hypothesis indicates whether the researcher tests for a positive or negative effect by including either the greater than (">") or less than ("<") sign.

Non-directional hypothesis

We refer to the alternative hypothesis in a statistical research question as a non-directional hypothesis. It includes the not equal ("≠") sign to show that the research tests whether or not an effect exists without specifying the effect's direction (positive or negative).

Associative hypothesis

The term associative hypothesis assumes a link between two variables but stops short of stating that one variable impacts the other. Academic statistical literature asserts in this sense that correlation does not imply causation. So, although the hypothesis notes the correlation between two variables – the independent and dependent - it does not predict how the two interact.

Logical hypothesis

Typically used in philosophy rather than science, researchers can't test a logical hypothesis because the technology or data set doesn't yet exist. A logical hypothesis uses logic as the basis of its assumptions. 

In some cases, a logical hypothesis can become an empirical hypothesis once technology provides an opportunity for testing. Until that time, the question remains too expensive or complex to address. Note that a logical hypothesis is not a statistical hypothesis.

Empirical hypothesis

When we consider the opposite of a logical hypothesis, we call this an empirical or working hypothesis. This type of hypothesis considers a scientifically measurable question. A researcher can consider and test an empirical hypothesis through replicable tests, observations, and measurements.

Statistical hypothesis

The term statistical hypothesis refers to a test of a theory that uses representative statistical models to test relationships between variables to draw conclusions regarding a large population. This requires an existing large data set, commonly referred to as big data, or implementing a survey to obtain original statistical information to form a data set for the study. 

Testing this type of hypothesis requires the use of random samples. Note that the null and alternative hypotheses are used in statistical hypothesis testing.

Causal hypothesis

The term causal hypothesis refers to a research hypothesis that tests a cause-and-effect relationship. A causal hypothesis is utilized when conducting experimental or quasi-experimental research.

Descriptive hypothesis

The term descriptive hypothesis refers to a research hypothesis used in non-experimental research, specifying an influence in the relationship between two variables.

• What makes an effective research hypothesis?

An effective research hypothesis offers a clearly defined, specific statement, using simple wording that contains no assumptions or generalizations, and that you can test. A well-written hypothesis should predict the tested relationship and its outcome. It contains zero ambiguity and offers results you can observe and test. 

The research hypothesis should address a question relevant to a research area. Overall, your research hypothesis needs the following essentials:

Hypothesis Essential #1: Specificity & Clarity

Hypothesis Essential #2: Testability (Provability)

• How to develop a good research hypothesis

In developing your hypothesis statements, you must pre-plan some of your statistical analysis. Once you decide on your problem to examine, determine three aspects:

the parameter you'll test

the test's direction (left-tailed, right-tailed, or non-directional)

the hypothesized parameter value

Any quantitative research includes a hypothesized parameter value of a mean, a proportion, or the difference between two proportions. Here's how to note each parameter:

Single mean (μ)

Paired means (μd)

Single proportion (p)

Difference between two independent means (μ1−μ2)

Difference between two proportions (p1−p2)

Simple linear regression slope (β)

Correlation (ρ)

Defining these parameters and determining whether you want to test the mean, proportion, or differences helps you determine the statistical tests you'll conduct to analyze your data. When writing your hypothesis, you only need to decide which parameter to test and in what overarching way.

The null research hypothesis must include everyday language, in a single sentence, stating the problem you want to solve. Write it as an if-then statement with defined variables. Write an alternative research hypothesis that states the opposite.

• What is the correct format for writing a hypothesis?

The following example shows the proper format and textual content of a hypothesis. It follows commonly accepted academic standards.

Null hypothesis (H0): High school students who participate in varsity sports as opposed to those who do not, fail to score higher on leadership tests than students who do not participate.

Alternative hypothesis (H1): High school students who play a varsity sport as opposed to those who do not participate in team athletics will score higher on leadership tests than students who do not participate in athletics.

The research question tests the correlation between varsity sports participation and leadership qualities expressed as a score on leadership tests. It compares the population of athletes to non-athletes.

• What are the five steps of a hypothesis?

Once you decide on the specific problem or question you want to address, you can write your research hypothesis. Use this five-step system to hone your null hypothesis and generate your alternative hypothesis.

Step 1 : Create your research question. This topic should interest and excite you; answering it provides relevant information to an industry or academic area.

Step 2 : Conduct a literature review to gather essential existing research.

Step 3 : Write a clear, strong, simply worded sentence that explains your test parameter, test direction, and hypothesized parameter.

Step 4 : Read it a few times. Have others read it and ask them what they think it means. Refine your statement accordingly until it becomes understandable to everyone. While not everyone can or will comprehend every research study conducted, any person from the general population should be able to read your hypothesis and alternative hypothesis and understand the essential question you want to answer.

Step 5 : Re-write your null hypothesis until it reads simply and understandably. Write your alternative hypothesis.

What is the Red Queen hypothesis?

Some hypotheses are well-known, such as the Red Queen hypothesis. Choose your wording carefully, since you could become like the famed scientist Dr. Leigh Van Valen. In 1973, Dr. Van Valen proposed the Red Queen hypothesis to describe coevolutionary activity, specifically reciprocal evolutionary effects between species to explain extinction rates in the fossil record. 

Essentially, Van Valen theorized that to survive, each species remains in a constant state of adaptation, evolution, and proliferation, and constantly competes for survival alongside other species doing the same. Only by doing this can a species avoid extinction. Van Valen took the hypothesis title from the Lewis Carroll book, "Through the Looking Glass," which contains a key character named the Red Queen who explains to Alice that for all of her running, she's merely running in place.

• Getting started with your research

In conclusion, once you write your null hypothesis (H0) and an alternative hypothesis (Ha), you’ve essentially authored the elevator pitch of your research. These two one-sentence statements describe your topic in simple, understandable terms that both professionals and laymen can understand. They provide the starting point of your research project.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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What is and How to Write a Good Hypothesis in Research?

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Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

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It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

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Scientific Research Methods

28.2 about hypotheses and assumptions.

Two hypotheses are made about the population parameter:

• The null hypothesis \(H_0\) ; and
• The alternative hypothesis \(H_1\) .

28.2.1 Null hypotheses

Hypotheses always concern a population parameter . Hypothesising, for example, that the sample mean body temperature is equal to \(37.0^\circ\text{C}\) is pointless, because it clearly isn’t: the sample mean is \(36.8051^\circ\text{C}\) . Besides, the RQ is about the unknown population : the P in P OCI stands for P opulation.

The null hypothesis \(H_0\) offers one possible reason why the value of the sample statistic (such as the sample mean) is not the same as the value of the proposed population parameter (such as the population mean): sampling variation . Every sample is different, and so the sample statistic will vary from sample to sample; it may not be equal to the population parameter , just because of the sample used by chance. Null hypotheses always have an ‘equals’ in them (for example, the population mean equals 100, is less than or equal to 100, or is more than or equal to 100), because (as part of the decision making process ), something specific must be assumed for the population parameter.

The parameter can take many different forms, depending on the context. The null hypothesis about the parameter is the default value of that parameter; for example,

• there is no difference between the parameter value in two (or more) groups;
• there is no change in the parameter value; or
• there is no relationship as measured by a parameter value.

28.2.2 Alternative hypotheses

The other hypothesis is called the alternative hypothesis \(H_1\) . The alternative hypothesis offers another possible reason why the value of the sample statistic (such as the sample mean) is not the same as the value of the proposed population parameter (such as the population mean). The alternative hypothesis proposes that the value of the population parameter really is not the value claimed in the null hypothesis.

Alternative hypotheses can be one-tailed or two-tailed . A two -tailed alternative hypothesis means, for example, that the population mean could be either smaller or larger than what is claimed. A one -tailed alternative hypothesis admits only one of those two possibilities. Most (but not all) hypothesis tests are two-tailed.

The decision about whether the alternative hypothesis is one- or two-tailed is made by reading the RQ ( not by looking at the data). Indeed, the RQ and hypotheses should (in principle) be formed before the data are obtained , or at least before looking at the data if the data are already collected.

The ideas are the same whether the alternative hypothesis is one- or two-tailed: based on the data and the sample statistic, a decision is to be made about whether the alternative hypotheses is supported by the data.

Example 28.1 (Alternative hypotheses) For the body-temperature study, the alternative hypothesis is two-tailed : The RQ asks if the population mean is \(37.0^\circ\text{C}\) or not . That is, two possibilities are considered: that \(\mu\) could be either larger or smaller than \(37.0^\circ\text{C}\) .

Important points about forming hypotheses:

• Hypotheses always concern a population parameter.
• Null hypotheses always contain an ‘equals.’
• Alternative hypothesis are one-tailed or two-tailed, depending on the RQ.
• Hypotheses emerge from the RQ (not the data): The RQ and the hypotheses could be written down before collecting the data.

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7.3: The Research Hypothesis and the Null Hypothesis

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• Page ID 18038

• Michelle Oja
• Taft College

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Hypotheses are predictions of expected findings.

The Research Hypothesis

A research hypothesis is a mathematical way of stating a research question.  A research hypothesis names the groups (we'll start with a sample and a population), what was measured, and which we think will have a higher mean.  The last one gives the research hypothesis a direction.  In other words, a research hypothesis should include:

• The name of the groups being compared.  This is sometimes considered the IV.
• What was measured.  This is the DV.
• Which group are we predicting will have the higher mean.  

There are two types of research hypotheses related to sample means and population means:  Directional Research Hypotheses and Non-Directional Research Hypotheses

Directional Research Hypothesis

If we expect our obtained sample mean to be above or below the other group's mean (the population mean, for example), we have a directional hypothesis. There are two options:

• Symbol:       \( \displaystyle \bar{X} > \mu \)
• (The mean of the sample is greater than than the mean of the population.)
• Symbol:     \( \displaystyle \bar{X} < \mu \)
• (The mean of the sample is less than than mean of the population.)

Example \(\PageIndex{1}\)

A study by Blackwell, Trzesniewski, and Dweck (2007) measured growth mindset and how long the junior high student participants spent on their math homework.  What’s a directional hypothesis for how scoring higher on growth mindset (compared to the population of junior high students) would be related to how long students spent on their homework?  Write this out in words and symbols.

Answer in Words:            Students who scored high on growth mindset would spend more time on their homework than the population of junior high students.

Answer in Symbols:         \( \displaystyle \bar{X} > \mu \) 

Non-Directional Research Hypothesis

A non-directional hypothesis states that the means will be different, but does not specify which will be higher.  In reality, there is rarely a situation in which we actually don't want one group to be higher than the other, so we will focus on directional research hypotheses.  There is only one option for a non-directional research hypothesis: "The sample mean differs from the population mean."  These types of research hypotheses don’t give a direction, the hypothesis doesn’t say which will be higher or lower.

A non-directional research hypothesis in symbols should look like this:    \( \displaystyle \bar{X} \neq \mu \) (The mean of the sample is not equal to the mean of the population).

Exercise \(\PageIndex{1}\)

What’s a non-directional hypothesis for how scoring higher on growth mindset higher on growth mindset (compared to the population of junior high students) would be related to how long students spent on their homework (Blackwell, Trzesniewski, & Dweck, 2007)?  Write this out in words and symbols.

Answer in Words:            Students who scored high on growth mindset would spend a different amount of time on their homework than the population of junior high students.

Answer in Symbols:        \( \displaystyle \bar{X} \neq \mu \) 

See how a non-directional research hypothesis doesn't really make sense?  The big issue is not if the two groups differ, but if one group seems to improve what was measured (if having a growth mindset leads to more time spent on math homework).  This textbook will only use directional research hypotheses because researchers almost always have a predicted direction (meaning that we almost always know which group we think will score higher).

The Null Hypothesis

The hypothesis that an apparent effect is due to chance is called the null hypothesis, written \(H_0\) (“H-naught”). We usually test this through comparing an experimental group to a comparison (control) group.  This null hypothesis can be written as:

\[\mathrm{H}_{0}: \bar{X} = \mu \nonumber \]

For most of this textbook, the null hypothesis is that the means of the two groups are similar.  Much later, the null hypothesis will be that there is no relationship between the two groups.  Either way, remember that a null hypothesis is always saying that nothing is different.  

This is where descriptive statistics diverge from inferential statistics.  We know what the value of \(\overline{\mathrm{X}}\) is – it’s not a mystery or a question, it is what we observed from the sample.  What we are using inferential statistics to do is infer whether this sample's descriptive statistics probably represents the population's descriptive statistics.  This is the null hypothesis, that the two groups are similar.  

Keep in mind that the null hypothesis is typically the opposite of the research hypothesis. A research hypothesis for the ESP example is that those in my sample who say that they have ESP would get more correct answers than the population would get correct, while the null hypothesis is that the average number correct for the two groups will be similar. 

In general, the null hypothesis is the idea that nothing is going on: there is no effect of our treatment, no relation between our variables, and no difference in our sample mean from what we expected about the population mean. This is always our baseline starting assumption, and it is what we seek to reject. If we are trying to treat depression, we want to find a difference in average symptoms between our treatment and control groups. If we are trying to predict job performance, we want to find a relation between conscientiousness and evaluation scores. However, until we have evidence against it, we must use the null hypothesis as our starting point.

In sum, the null hypothesis is always : There is no difference between the groups’ means OR There is no relationship between the variables .

In the next chapter, the null hypothesis is that there’s no difference between the sample mean   and population mean.  In other words:

• There is no mean difference between the sample and population.
• The mean of the sample is the same as the mean of a specific population.
• \(\mathrm{H}_{0}: \bar{X} = \mu \nonumber \)
• We expect our sample’s mean to be same as the population mean.

Exercise \(\PageIndex{2}\)

A study by Blackwell, Trzesniewski, and Dweck (2007) measured growth mindset and how long the junior high student participants spent on their math homework.  What’s the null hypothesis for scoring higher on growth mindset (compared to the population of junior high students) and how long students spent on their homework?  Write this out in words and symbols.

Answer in Words:            Students who scored high on growth mindset would spend a similar amount of time on their homework as the population of junior high students.

Answer in Symbols:    \( \bar{X} = \mu \)

Contributors and Attributions

Foster et al.  (University of Missouri-St. Louis, Rice University, & University of Houston, Downtown Campus)

Dr. MO ( Taft College )

• [email protected]

Assumptions in Research: Foundation, 5 Types, and Impact

An assumption is a belief, thing, or statement that is taken as true by the researcher. It is not tested in research because these statements are the cornerstone of whole research. These are universally accepted and sufficiently well demonstrated that the researcher can build on them.

They are a fundamental part of the human experience. People make them in their everyday decisions and experiences. If we do not consider these assumptions, our research will not proceed any further.

Our inferences or conclusions are often based on them, and sometimes we do not think about it critically. Nevertheless, a critical thinker pays close attention to these assumptions, recognizing that they can be flawed or misinformed. Merely presuming something’s validity doesn’t guarantee its accuracy. Just because we assume something is true doesn’t mean it is.

In research, we must think carefully about them when finding and analyzing information, but we also must think carefully about the assumptions of others. When looking at a website or a scholarly article, we should always consider the author’s assumptions, whether the author has taken them logically.

However, when one person believes one thing to be true, it may be somewhat different from what another person believes to be true. Although the well-established assumptions are firmly rooted in prior research, most of us tend to accept those assumptions that square with our own personal or professional views of the world without questioning the extent to which they have been or are capable of being verified. In addition, assumptions are not always easy to state. Seasoned researchers may not consider it seemly to admit that fact, but beginning researchers are quick to acknowledge the difficulty and to ask where the dividing line falls between assumptions and hypotheses. For example, the statement that memory loss occurs with aging may be accepted as an assumption by some but as a hypothesis for investigation by others.

Assumptions are things that are accepted as true; any scholar reading our paper will assume that certain aspects of our study are true, like population, statistical test, research design, or other delimitations. For example, if I tell my friend that the jungle is my favorite place, he will assume that I have never encountered a lion in the jungle. It’s assumed that I go there for walks and recreation. Because most assumptions are not discussed in text, assumptions that are discussed in text are discussed in the context of the limitations of our study, which is typically in the discussion section.

This is important, because both assumptions and limitations affect the inferences we can draw from your study. One prevalent assumption often made in survey research involves expecting honesty and truthful answers. However, for certain sensitive questions this assumption may be more difficult to accept, in this case it would be described as a limitation of the study.

For example, asking people to report their criminal or sexual behavior in a survey may not be as reliable as asking people to report their eating habits. It is important to remember that our limitations and assumptions should not contradict one another. For instance, if we state that generalizability is a limitation of our study given that our sample was limited to one city in Pakistan, then we should not claim generalizability to Pakistan population as an assumption of our study.

In quantitative research designs, statistical models come with accompanying assumptions, which can vary in their stringency. These assumptions typically pertain to data characteristics, including distributions, correlations, and variable types. Violating these assumptions can lead to drastically invalid results, though this often depends on sample size and other considerations.

Table of Contents

Types of assumptions, 1. universal .

These assumptions are believed to be universally accepted and considered as true by large part of society. To test these assumptions is a very difficult task.

For example: There is a super natural force which holds this whole universe.

2.  Based On Theories

If a researcher is working on a theory, the assumptions used in that theory will also be the assumptions of this study.

For example: Research on atomic theory will take the assumptions of development of atomic theory.

3. Common Sense Assumptions

Some of the common sense assumptions are taken to conduct a research.

For example: Heart attack is more common in urban areas as compared to rural areas.

4. Warranted 

This assumption is supported by certain evidence.

For example: Regular walk can reduce obesity.

5. Unwarranted 

This assumption is not supported by evidences.

For example: God exists everywhere in this universe.

Examples in Research

• Sample is a true representative of population.
• It is a true experimental design.
• In comparison of two teaching methods, the behavior of students will be ideal and results are generalizable.
• We will receive true responses from respondents.
• During the experiment in laboratory, no hidden factors will affect the results of experiment.
• The equipment is functioning well and there is no error in equipment.

IDENTIFYING ASSUMPTION

When we make incorrect or unreasonable assumption during research, we will get false conclusions. So we should think that what assumption should be a part of thesis and what should not be. A good assumption is that which can be verified or justified. A bad one on the other hand cannot be verified or justified. The researcher must explain and give examples that the assumption made is true. For example, if the researcher is making an assumption that respondents will give honest responses to your questions, he or she must explain the data collection process and how will preserve anonymity and confidentiality to maximize the truthfulness.

DIFFERENCE BETWEEN HYPOTHESIS AND ASSUMPTION

A hypothesis is an intelligent guess which establishes relationship between variables. On the other hand, assumption is statement or belief which is taken as true without any justification. Hypothesis is tested explicitly, and assumption is tested implicitly. Hypothesis passes through the stages of verification. Assumption specifies the existence of relationship between variables while hypothesis establishes this relationship.

Hypotheses and assumption are so close to each other that sometime they create confusion. Assumption is assumed true statement without having any firm explanation behind it. Hypothesis is an assumption which is taken to be true unless proved otherwise.

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

• Open access
• Published: 24 May 2024

Rosace : a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

• Jingyou Rao 1 ,
• Ruiqi Xin 2   na1 ,
• Christian Macdonald 3   na1 ,
• Matthew K. Howard 3 , 4 , 5 ,
• Gabriella O. Estevam 3 , 4 ,
• Sook Wah Yee 3 ,
• Mingsen Wang 6 ,
• James S. Fraser 3 , 7 ,
• Willow Coyote-Maestas 3 , 7 &
• Harold Pimentel   ORCID: orcid.org/0000-0001-8556-2499 1 , 8 , 9  

Genome Biology volume  25 , Article number:  138 ( 2024 ) Cite this article

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Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p -values cannot be correctly calibrated when treating variants independently. We propose Rosace , a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.

Understanding how protein function is encoded at the residue level is a central challenge in modern protein science. Mutations can cause diseases and drive evolution through perturbing protein function in a myriad of ways, such as by altering its conformational ensemble and stability or its interaction with ligands and binding partners. In these contexts, mutations may result in a loss of function, gain of function, or a neutral phenotype (i.e., no discernable effects). Mutations also often exert effects across multiple phenotypes, and these perturbations can ultimately propagate to alter complex processes in cell biology and physiology. Reverse genetics approaches offer a powerful handle for researchers to investigate biology via introducing mutations and observing the resulting phenotypic changes.

Deep mutational scanning (DMS) is a technique for systematically determining the effect of a large library of mutations individually on a phenotype of interest by performing pooled assays and measuring the relative effects of each variant (Fig.  1 A) [ 1 , 2 , 3 ]. It has improved clinical variant interpretation [ 4 ] and provided insights into the biophysical modeling and mechanistic models of genetic variants [ 5 ]. Taking enzymes as an example, these phenotypes could include catalytic activity [ 6 ] or stability [ 7 , 8 ]. For a transcription factor, the phenotype could be DNA binding specificity or transcriptional activity [ 9 ]. The relevant phenotype for a membrane transporter might be folding and trafficking or substrate transport [ 10 ]. These phenotypes are often captured by growth-based [ 7 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], binding-based [ 9 , 17 , 18 ], or fluorescence-based assays [ 8 , 10 , 19 ]. Those experiments are inherently differently designed and merit separate analysis frameworks. In growth-based assays, the relative growth rates of cells are of interest. In a binding-based assay, the selection probabilities are of interest. In fluorescence-based assays, changes to the distribution of reporter gene expression are measured. In this paper, we focus solely on growth-based screens.

Deep mutational scanning and overview of Rosace  framework. A Each amino acid of the selected protein sequence is mutated to another mutant in deep mutational scanning. B Cells carrying different variants are grown in the same pool under selection pressure. At each time point, cells are sequenced to output the count table. Replications can be produced either pre-transfection or post-transfection. C Rosace is an R package that accepts input from the raw sequencing count table and outputs the posterior distribution of functional score

In a growth-based DMS experiment, we grow a pool of cells carrying different variants under a selective pressure linked to gene function. At set intervals, we sequence the cells to identify each variant’s frequency in the pool. The change in the frequency over the course of the experiment, from initial frequencies to subsequent measurements, serves as a metric of the variant’s functional effects (Fig.  1 B). The functional score is often computed for each variant in the DMS screen and compared against those of synonymous mutations or wild-type cells to display the relative functional change of the protein caused by the mutation. Thus, reliable inference of functional scores is crucial to understanding both individual mutations and at which residue location variants tend to have significant functional effects.

The main challenge of functional score inference is that even under the simplest model, there are at least two estimators required for each mutation (mean and variance of functional change), and in practice, it is rare to have more than three replicates. As a result, it has been posited that under naïve estimators that have been commonly employed, there are likely issues with the false discovery rate and the statistical power of detecting mutations that significantly change the function of the protein [ 20 ]. Regardless, incorporating domain-specific assumptions is required to make inference tractable with few samples and thousands of parameters.

To alleviate the small-sample-size inference problem in DMS, four commonly used methods have been developed: dms_tools [ 21 ], Enrich2 [ 18 ], DiMSum [ 20 ], and EMPIRIC [ 22 ]. dms_tools uses Bayesian inference for reliable inference. However, rather than giving a score to each variant, dms_tools generates a score for each amino acid at each position, assuming linear addition of multiple mutation effects and ignoring epistasis coupling. Thus, dms_tools is not directly comparable to other methods and is excluded from our benchmarking analysis. Enrich2 simplifies the variance estimator by assuming that counts are Poisson-distributed (the variance being equal to the mean) and combines the replicates using a random-effect model. DiMSum , however, argues that the assumption in Enrich2 is not enough to control type-I error. As a result, DiMSum builds upon Enrich2 and includes additional variance terms to model the over-dispersion of sequencing counts. However, as presented in Faure et al. 2020 [ 20 ], this ratio-based method only applies to the DMS screen with one round of selection, while many DMS screens have more than two rounds of selection (i.e., sampling at multiple time points) [ 10 , 11 , 23 ]. Alternatively, EMPIRIC fits a Bayesian model that infers each variant separately with non-informative uniform prior to all parameters and thus does not shrink the estimates to robustly correct the variance in estimates due to the small sample size. Further, the model does not accommodate multiple replicates. In addition, mutscan [ 24 ], a recently developed R package for DMS analysis, employed two established statistical models edgeR and limma-voom . However, these two methods were originally designed for RNA-seq data and the data generation process for DMS is very different. One of the key differences is consistency among replicates. In RNA-seq, gene expression is relatively consistent across replicates under the same condition, while in DMS, counts of variants can vary much since the a priori representation in the initial variant library can be vastly inconsistent among replicates.

While these methods provide reasonable regularization of the score’s variance, additional information can further improve the prior. One solution is incorporating residue position information. It has been noted that amino acids in particular regions have an oversized effect on the protein’s function, and other frameworks have incorporated positions for various purposes. In the form of hidden Markov models (HMMs) and position-specific scoring matrices (PSSMs), this is the basis for the sensitive detection of homology in protein sequences [ 25 ]. These results directly imply that variants at the same position likely share some similarities in their behavior and thus that incorporating local information into modeling might produce more robust inferences. However, no existing methods have incorporated residue position information into their models yet.

To overcome these limitations, we present Rosace , the first growth-based DMS method that incorporates local positional information to increase inference performance. Rosace implements a hierarchical model that parameterizes each variant’s effect as a function of the positional effect, thus providing a way to incorporate both position-specific information and shrinkage into the model. Additionally, we developed Rosette , a simulation framework that attempts to simulate several properties of DMS such as bimodality, similarities in behavior across similar substitutions, and the overdispersion of counts. Compared to previous simulation frameworks such as the one in Enrich2 , Rosette uses parameters directly inferred from the specific input experiment and generates counts that reflect the true level of noise in the real experiment. We use Rosette to simulate several screening modalities and show that our inference method, Rosace , exhibits higher power and controls the false discovery rate (FDR) better on average than existing methods. Importantly, Rosace and Rosette are not two views of the same model— Rosette is based on a set of assumptions that are different from or even opposite to those of Rosace . Rosace ’s ability to accommodate data generated under different assumptions shows its robustness. Finally, we run Rosace on real datasets and it shows a much lower FDR than existing methods while maintaining similar power on experimentally validated positive controls.

Overview of Rosace  framework

Rosace is a Bayesian framework for analyzing growth-based deep mutational scanning data, producing variant-level estimates from sequencing counts. The full (position-aware) method requires as input the raw sequencing counts and the position labels of variants. It outputs the posterior distribution of variants’ functional scores, which can be further evaluated to conduct hypothesis testing, plotting, and other downstream analyses (Fig.  1 C). If the position label is hard to acquire with heuristics, for example, in the case of random multiple-mutation data, position-unaware Rosace model can be run without position label input. Rosace is available as an R package. To generate the input of Rosace from sequencing reads, we share a Snakemake workflow dubbed Dumpling for short-read-based experiments in the GitHub repository described in the “ Methods ” section. Additionally, Rosace supports input count data processed from Enrich2 [ 18 ] for other protocols such as barcoded sequencing libraries.

Rosace  hierarchical model with positional information and score shrinkage

Here, we begin by motivating the use of positional information. Next, we describe the intuition of how we use the positional information. Finally, we describe the remaining dimensions of shrinkage which assist in robust estimates with few experiment replicates.

A variant is herein defined as the amino acid identity at a position in a protein, where that identity may differ from the wild-type sequence. In this context, synonymous, missense, nonsense, and indel variants are all considered and can be processed by Rosace (see the “ Methods ” section for details). The sequence position of a variant p ( v ) provides information on the functional effects to the protein from the variant. We define the position-level functional score \(\phi _{p(v)}\) as the mean functional score of all variants on a given position.

To motivate the use of positional information, we take the posterior distribution of the position-level functional score estimated from a real DMS experiment, a cytotoxicity-based growth screen of a human transporter, OCT1 (Fig.  2 A). In this experiment, variants with decreased activity are expected to increase in abundance, as they lose the ability to import a cytotoxic substrate during selection, and variants with increased activity will decrease in abundance similarly. We observe that most position-level score estimates \(\widehat{\phi }_{p(v)}\) significantly deviate from the mean, implying that position has material idiosyncratic variation and thus carries information about the protein’s functional architecture.

Rosace shares information at the same position to inform variant effects. A Smoothed position-specific score (sliding window = 5) across positions from OCT1 cytotoxicity screen. Red dotted lines at score = 0 (neutral position). B A conceptual view of the Rosace generative model. Each position has an overall effect, from which variant effects are conferred. Note the prior is wide enough to allow effects that do not follow the mean. Wild-type score distribution is assumed to be at 0. C Plate model representation of Rosace . See the “ Methods ” section for the description of parameters

To incorporate the positional information into our model, we introduce a position-specific score \(\phi _{p(v)}\) where p ( v ) maps variant v to its amino acid position. The variant-specific score \(\beta _v\) is regularized and controlled by the value of \(\phi _{p(v)}\) . To illustrate the point, we conceptually categorize position into three types: positively selected ( \(\phi _{p(v)} \gg 0\) ), (nearly) neutral ( \(\phi _{p(v)} \approx 0\) ), and negatively selected ( \(\phi _{p(v)} \ll 0\) ) (Fig.  2 B). Variants in a positively selected position tend to have scores centered around the positive mean estimate of \(\phi _{p(v)}\) , and vice versa for the negatively selected position. Variants in a neutral position tend to be statistically non-significant as the region might not be important to the measured phenotype.

Regularization of the score’s variance is achieved mainly by sharing information across variants within the position and asserting weakly informative priors on the parameters (Fig.  2 C). Functional scores of the variants within the position are drawn from the same set of parameters \(\phi _{p(v)}\) and \(\sigma _{p(v)}\) . The error term \(\epsilon _{g(v)}\) in the linear regression on normalized counts is also shared in the mean count group (see the “ Methods ” section) to prevent biased estimation of the error and incorporate mean-variance relationship commonly modeled in RNA-seq [ 26 , 27 ]. Importantly, while we use the position information to center the prior, the prior is weak enough to allow variants at a position to deviate from the mean. For example, we show that the nonsense variants indeed deviate from the positional mean (Additional file 1: Fig. S3). The variant-level intercept \(b_v\) is given a strong prior with a tight distribution centered at 0 to prevent over-fitting.

Rosace  performance on various datasets

To test the performance of Rosace , we ran Rosace along with Enrich2 , mutscan (both limma-voom and edgeR ), DiMSum , and simple linear regression (the naïve method) on the OCT1 cytotoxicity screen. DiMSum cannot analyze data with three selection rounds, so we ran DiMSum with only the first two time points. The data is pre-processed with wild-type normalization for all three methods. The analysis is done on all subsets of three replicates ( \(\{1\}, \{2\}, \{3\}, \{1,2\}, \{1,3\}, \{2,3\}, \{1,2,3\}\) ).

While we do not have a set of true negative control variants, we assume most synonymous mutations would not change the phenotype, and thus, we use synonymous mutation as a proxy for negative controls. We compute the percentage of significant synonymous mutations called by the hypothesis testing as one representation of the false discovery rate (FDR). The variants are ranked based on the hypothesis testing statistics from the method ( p -value for frequentist methods and local false sign rate [ 28 ], or lfsr ) for Bayesian methods). In an ideal scenario with no noise, the line of ranked variants by FDR is flat at 0 and slowly rises after all true variants with effect are called. Rosace has a very flat segment among the top 25% of the ranked variants compared to DiMSum , Enrich2 , and the naïve method and keeps the FDR lower than mutscan(limma) and mutscan(edgeR) until the end (Fig.  3 A). Importantly, we note that the Rosace curve moves only slightly from 1 replicate to 3 replicates, while the other methods shift more, implying that the change in the number of synonymous mutations called is minor for Rosace , despite having fewer replicates (Fig.  3 A).

False discovery rate and sensitivity on OCT1 cytotoxicity data. A Percent of synonymous mutations called (false discovery rate) versus ranked variants by hypothesis testing. The left panel is from taking the mean of analysis of the three individual replicates. Ideally, the line would be flat at 0 until all the variants with true effects are discovered. B Number of validated variants called (in total 10) versus number of replicates. If only 1 or 2 replicates are used, we iterate through all possible combinations. For example, the three points for Rosace on 2 replicates use Replicate \(\{1, 2\}\) , \(\{1, 3\}\) , and \(\{2, 3\}\) respectively. (DiMSum can only process two time points, and thus is disadvantaged in experiments such as OCT1)

While lower FDR may result in lower power in the method, we show that Rosace is consistently powerful in detecting the OCT1-positive control variants. Yee et al. [ 10 ] conducted lower-throughput radioligand uptake experiments in HEK293T cells and validated 10 variants that have a loss-of-function or gain-of-function phenotype. We use the number of validated variants to approximate the power of the method. As shown in Fig.  3 B, Rosace has comparable power to Enrich2 , mutscan(limma) , and mutscan(edgeR) regardless of the number of replicates, while the naïve method is unable to detect anything in the case of one replicate. Rosace calls significantly fewer synonymous mutations than every other method while maintaining high power, showing that Rosace is robust in real data.

In OCT1, loss of function leads to enrichment rather than depletion, which is relatively uncommon. To complement findings on OCT1, we conducted a similar analysis on the kinase MET data [ 11 ] (3 replicates, 3 selection rounds), whose loss of function leads to depletion. Applied to this dataset, Rosace and its position-unaware version have comparable power to Enrich2 , mutscan(limma) , and mutscan(edgeR) with any number of replicates used, and the naïve method remains less powerful than other methods, especially with one replicate only. Consistent with OCT1, Rosace again calls fewer synonymous mutations and better controls the false discovery rate. The results are visualized in the Supplementary Figures (Additional file 1: Figs. S12-15).

To test Rosace performance on diverse datasets, we also ran all methods on the CARD11 data [ 14 ] (5 replicates, 1 selection round), the MSH2 data [ 12 ] (3 replicates, 1 selection round), the BRCA1 data [ 13 ] (2 replicates, 2 selection rounds), and the BRCA1-RING data [ 23 ] (6 replicates, 5 selection rounds) (Table S1). In addition to those human protein datasets, we also applied Rosace to a bacterial protein, Cohesin [ 29 ] (1 replicate, 1 selection round) (Table S1). We use the pathogenic and benign variants in ClinVar [ 30 ], EVE [ 31 ], and AlphaMissense [ 32 ] to provide a proxy of positive and negative control variants. Rosace consistently shows high sensitivity in detecting the positive control variants in all three datasets while controlling the false discovery rate (Additional file 1: Figs. S5-S11). Noting that the number of clinically verified variants is limited and those identified in the prediction models usually have extreme effects, we do not observe a large difference between the methods’ performance.

To alleviate a potential concern that the position-level shrinkage given by Rosace is too large, we plot the functional scores calculated by Rosace against those by Enrich2 across several DMS datasets (Additional file 1: Figs. S2-4). We find that the synonymous variants’ functional scores are similar in magnitude to those of other variants, so synonymous variants are not shrunken too strongly to zero. We also find that stop codon and indel variants have consistently significant effect scores, implying that position-level shrinkage is not so strong that those variants’ effects are neutralized. This result implies that the position prior benefits the model mainly through a more stable standard error estimate enabling improved prioritization as a function of local false sign rate or other posterior ranking criteria that are a function of the variance.

Rosette : DMS data simulation which matches marginal distributions from real DMS data

To further benchmark the performance of Rosace and other related methods, we propose a new simulation framework called Rosette , which generates DMS data using parameters directly inferred from the real experiment to gain the flexibility of mimicking the overall structure of most growth-based DMS screen data (Fig.  4 A).

Rosette simulation framework preserves the overall structure of growth-based DMS screens. The plots show the result of using OCT1 data as input. A Rosette generates summary statistics from real data and simulates the sequencing count. B Generative model for Rosette simulation. C The distribution of real and predicted functional scores is similar. D , E Five summary statistics are needed for Rosette

Intuitively, if we construct a simulation that closely follows the assumptions of our model, our model should have outstanding performance. To facilitate a fair comparison with other methods, the simulation presented here is not aligned with the assumptions made in Rosace . In fact, the central assumption that variant position carries information is violated by construction to showcase the robustness of Rosace .

To re-clarify the terminology used throughout this paper, “mutant” refers to the substitution, insertion, or deletion of amino acids. A position-mutant pair is considered a variant. Mutants are categorized into mutant groups with hierarchical clustering schemes or predefined criteria (our model uses the former that are expected to align with the biophysical properties of amino acids). Variants are grouped in two ways: (1) by their functional change to the protein, namely neutral, loss-of-function (LOF), or gain-of-function (GOF), referred to as “variant groups,” and (2) by the mean of the raw sequencing counts across replicates, referred to as “variant mean groups.”

Rosette calculates two summary statistics from the raw sequencing counts (dispersion of the sequencing count \(\eta\) and dispersion of the variant library \(\eta _0\) ) (Fig.  4 D) and three others from the score estimates (the proportion of each mutant group \(\varvec{p}\) , the functional score’s distribution of each variant group \(\varvec{\theta }\) , and the weight of each variant group \(\varvec{\alpha }\) ) (Fig.  4 E). Since we are only learning the distribution of the scores instead of the functional characteristics of individual variants, the score estimates can be naïve (e.g., simple linear regression) or more complicated (e.g.,  Rosace ).

The dispersion of the sequencing counts \(\eta\) measures how much variability in variant representation there is in the entire experimental procedure, during both cell culture and sequencing. When \(\eta\) goes to infinity, it means that the sequencing count is almost the same as the expected true cell count (no over-dispersion). When \(\eta\) is small, it shows an over-dispersion of the sequencing count. In an ideal experiment with no over-dispersion, the proportion of synonymous mutations should be invariant to time due to the absence of functional changes. However, from the real data, we have observed a large variability of proportion changes within the synonymous mutations at different selection rounds, which is attributed to over-dispersion and cannot be explained by a simple multinomial distribution in existing simulation frameworks (Additional file 1: Fig. S1). Indeed, all methods, including the naïve method, achieve near-perfect performance in the Enrich2 simulations with a correlation score greater than 0.99 (Additional file 1: Fig. S27). Therefore, we choose to model the sequencing step with a Dirichlet-Multinomial distribution that includes \(\eta\) as the dispersion parameter.

The dispersion of variant library \(\eta _0\) measures how much variability already exists in variant representation before the cell selection. Theoretically, each variant would have around the same number of cells at the initial time point. However, due to the imbalance during the variant library generation process and the cell culture of the initial population that might already be under selection, we sometimes see a wide dispersion of counts across variants. To estimate this dispersion, we fit a Dirichlet-Multinomial distribution under the assumption that the variants in the cell pool at the initial time point should have equal proportions.

The distribution and the structure of the underlying true functional score across variants are controlled by the rest of the summary statistics. We make a few assumptions here. First, the functional score distribution of mutants across positions (or a row in the heatmap (Fig.  4 A)) is different, but within the mutant group, the mutants are independent and identically distributed (or exchangeable). We estimate the mutant group by hierarchical clustering with distance defined by empirical Jenson-Shannon Divergence and record its proportion \(\hat{\varvec{p}}\) . Second, each variant belongs to the neutral hypothesis (score close to 0, similar to synonymous mutations) or the alternative hypothesis (away from 0, different from synonymous mutations). The number of the variant group can be 1–3 (neutral, GOF, and LOF) based on the number of modes in the marginal functional score distribution, and the variants within a variant group are exchangeable. We estimate the borderline of the variant group by Gaussian mixture clustering and fit the distribution parameter \(\hat{\varvec{\theta }}\) . Finally, we assume that the positions are independent. While this is a simplifying assumption, to consider the relationship between positions, we would need to incorporate additional assumptions about the functional region of the protein. As a result, we treat the positions as exchangeable and model the proportion of variant group identity (neutral, GOF, LOF) in each mutant group by a Dirichlet distribution with parameter \(\hat{\varvec{\alpha }}\) .

To simulate the sequencing count from the summary statistics, we use a generative model that mimics the experiment process and is completely different from the Rosace inference model for fair benchmarking. We first draw the functional score of each variant \(\beta _v\) from the structure described in the summary statistics and the ones in the neutral group are set to be 0. Then, we map the functional score to its latent functional parameters: the cell growth rate in the growth screen. Next, we generate the cell count at a particular time point \(N_{v,t,r}\) by the cell count at the previous time point \(N_{v,t-1,r}\) and the latent functional parameters. Finally, the sequencing count is generated from a Dirichlet-Multinomial distribution with the summarized dispersion parameter and the cell count.

The simulation result shows that the simulated functional score distribution is comparable to the real experimental data (Fig.  4 C). We also demonstrate that the simulation is not particularly favorable to models containing positional information such as Rosace . From Fig.  4 E, we observe that in the simulation, the positional-level score is not as widespread as the real data. In addition, the positions with extreme scores (very positive scores in the OCT1 dataset) have reduced standard deviation in the real data, but not in the simulation (Additional file 1: Figs. S18d, S19d, S20d). As a result, we would expect the performance of Rosace to be better in real data than in the simulation.

Testing Rosace  false discovery control with Rosette  simulation

To test the performance of Rosace , we generate simulated data using Rosette from two distinctive growth-based assays: the transporter OCT1 data where LOF variants are positively selected [ 10 ] and the kinase MET data where LOF variants are negatively selected [ 11 ]. We further included the result of a saturation genome editing dataset CARD11 [ 14 ] in Additional file 1: Figs. S17-23. The OCT1 DMS screen measures the impact of variants on cytotoxic drug SM73 uptake mediated by the transporter OCT1. If a mutation causes the transporter protein to have decreased activity, the cells in the pool will import less substrate and thus die more slowly than wide-type or those with synonymous mutations, so the LOF variants would be positively selected. In the MET DMS screen, the kinase drives proliferation and cell growth in the BA/F3 mammalian cell line in the absence of IL-3 (interleukin-3) withdrawal. If the variant protein fails to function, the cells will die faster than the wild-type cells, so the LOF variants will be negatively selected. Both data sets have a clear separation of two modes in the functional score distribution (neutral and LOF) (Additional file 1: Figs. S18a, S19a). We benchmark Rosace with Enrich2 , mutscan(edgeR) , mutscan(limma) , and the naïve method in scenarios where we use 1 or all 3 of replicates and 1 or all 3 of selection rounds. DiMSum is benchmarked when there is only one round of selection because it is not designed to handle multiple rounds. Each scenario is repeated 10 times. The results of all methods show similar correlations with the latent growth rates (Additional file 1: Fig. S21), and thus, for benchmarking purposes, we focus on hypothesis testing.

We compare methods from a variant ranking point of view, comparing methods in terms of the number of false discoveries for any given number of variants selected to be LOF. This is because Rosace is a Bayesian framework that uses lfsr instead of p -values as the metric for variant selection and it is hard to translate lfsr to FDR for a hard threshold. Variants are ranked by adjusted p -values or lfsr (ascending). Methods that perform well will rank the truly LOF variants in the simulation ahead of non-LOF variants. In an ideal scenario with no noise, we would expect the line of ranked variants by FDR to be flat at 0 and slowly rise after all LOF variants are called. The results in Fig.  5 show that even though the position assumption is violated in the Rosette simulation, Rosace is robust enough to maintain a relatively low FDR in all simulation conditions.

Benchmark of false discovery control on Rosette simulation. Variants are ranked by hypothesis testing (adjusted p-values or lfsr ). The false discovery rate at each rank is computed as the proportion of neutral variants assuming all the variants till the rank cutoff are called significant. R is the number of replicates and T is the number of selection rounds. MET data is used for negative selection and OCT1 data for positive selection. Ideally, the line would be flat at 0 until the rank where all variants with true effects are discovered. (DiMSum can only process two time points and thus is disadvantaged in experiments with more than two time points, or one selection round)

Testing Rosace  power with Rosette  simulation

Next, we investigate the sensitivity of benchmarking methods at different FDR or lfsr cutoff. It is important to keep in mind that Rosace uses raw lfsr from the sampling result while all other methods use the Benjamini-Hochberg Procedure to control the false discovery rate. As a result, the cutoff for Rosace is on a different scale.

Rosace is the only method that displays high sensitivity in all conditions with a low false discovery rate. In the case of one selection round and three replicates ( \(T = 1\) and \(R = 3\) ), mutscan(edgeR) and mutscan(limma) do not have the power to detect any significant variants with the FDR threshold at 0.1. The same scenario occurs with DiMSum at negative selection and the naïve method at \(T = 3\) and \(R = 1\) (Fig.  6 ). The naïve method in general has very low power, while Enrich2 has a very inflated FDR.

Benchmark of sensitivity versus FDR. The upper row is simulated from a modified version of Rosette simulation to favor position-informed models. The bottom row is the results from standard Rosette . Circles, triangles, squares, and crosses represent LOF variant selection at adjusted p-values or lfsr of 0.001, 0.01, 0.05, and 0.10, respectively. Variants with the opposite sign of selection are then excluded. Ideally, for all methods besides Rosace , each symbol would lie directly above the corresponding symbol on the x-axis indicating true FDR. For Rosace , lfsr has no direct translation to FDR so the cutoff represented by the shape is theoretically on a different scale. (DiMSum can only process two time points, and thus is disadvantaged in experiments with more than two time points, or one selection round)

We benchmark Rosace on both Rosette simulations, which inherently violate the position assumption, and a modified version of Rosette that favors the position-informed model. We show that model misspecification does increase the false discovery rate of Rosace , but Rosace is robust enough to outperform all other methods (except for DiMSum with \(T = 1\) and \(R = 3\) and positive selection) even when the position assumption is strongly violated (Fig.  6 ).

One of Rosace ’s contributions is accounting for positional information in DMS analysis. The model assumes the prior information that variants on the same position have similar functional effects, resulting in higher sensitivity and better FDR. Furthermore, Rosace is also capable of incorporating other types of prior information on the similarity of variants.

Despite the value of positional information in statistical inference as demonstrated in this paper, it is unclear how multiple random mutations should be position-labeled. In this case, simple position heuristics are often unsatisfying, and one might argue that a position scalar should not cluster the variants in random mutagenesis experiments with large-scale in-frame insertion and deletion, such as those on viruses. These types of experiments are not the focus of this paper, but are still very important and require careful future research.

Another critique of Rosace is the extent of bias we introduce into the score inference through position-prior information. While it is certainly possible to introduce a large bias, Rosace was developed to be a robust model ensuring near-unbiased inference or prediction even when assumptions are not precisely complied with or even violated. We demonstrate the robustness of Rosace through our data simulation framework, Rosette . The generative procedures of Rosette explicitly violate the prior assumptions made by Rosace , but even with Rosette ’s data, Rosace can learn important information. We also show that the position-level shrinkage is not strong using real data, further manifesting the robustness of Rosace .

The development of DMS simulation frameworks such as Rosette can also drive experimental design. For example, to select the best number of time points and replicates with regard to the trade-off between statistical robustness and costs of the experiment, an experimentalist can conduct a pilot experiment and use its data to infer summary statistics through Rosette . Rosette will then generate simulations close to a real experiment. Experimentalists can find the optimal tool for data analysis given an experimental design by applying candidate tools to the simulation data. Similarly, given a data analysis framework, experimentalists can choose from multiple experiment designs by using Rosace to simulate all those experiments and observe if any designs have enough power to detect most of the LOF or GOF variants with a low false discovery rate.

This paper only applies our tool to growth screens, one of several functional phenotyping methods possible by DMS techniques. Another possibility is the binding experiment, where a portion of cells are selected at each time point. In this case, the expectation of functional scores computed by Rosace is a log transformation of the variant’s selection proportion [ 18 ], and one could potentially use Rosace for DMS analysis as in Enrich2 . The third method is fluorescently activated cell sorting (FACS-seq)—a branch of literature uses binned FACS-seq screens to sort the variant libraries based on protein phenotypes. Since the experiment has multiple bins, one can potentially capture the distributional change of molecular properties beyond mean shifting [ 8 , 10 , 19 , 33 ]. Although of different design, FACS-seq-based screens can also be analyzed using a framework similar to Rosace . Building such frameworks incorporating prior information for experiments beyond growth screens enables the community to exploit a wider range of experimental data.

As the function of a protein is rarely one-dimensional, one can measure multiple phenotypes of a variant in a set of experiments [ 10 , 16 , 34 ]. For example, the OCT1 data mentioned earlier [ 10 ] measures both the transporter surface expression from a FACS-seq screen and drug cytotoxicity with a growth screen. Multi-phenotype DMS experiments also call for analysis frameworks to accommodate multidimensional outcomes by modeling the interaction or the correlation of phenotypes of each variant. One successful attempt models the causal biophysical mechanism of protein folding and binding [ 35 ], and there are many more protein properties other than those two. A unifying framework for the multi-phenotype analysis remains unsolved and challenging. One needs to account for different experimental designs to directly compare scores between phenotypes, and carefully select inferred features most relevant to the scientific questions, requiring both efforts from the experimental and computational side. Nevertheless, we believe that the multi-phenotype analysis will eventually guide us to develop better mechanistic or probabilistic models for how mutations drive proteins in evolution, how they lead to malfunction and diseases, and how to better engineer new proteins.

Conclusions

We present Rosace , a Bayesian framework for analyzing growth-based deep mutational scanning data. In addition, we develop Rosette , a simulation framework that recapitulates the properties of actual DMS experiments, but relies on an orthogonal data generation process from Rosace . From both simulation and real data analysis, we show that Rosace has better FDR control and higher sensitivity compared to existing methods and that it provides reliable estimates for downstream analyses.

Pipeline: raw read to sequencing count

To facilitate the broader adoption of the Rosace framework for DMS experiments, we have developed a sequencing pipeline for short-read-based experiments using Snakemake which we dub Dumpling [ 36 ]. This pipeline handles directly sequenced single-variant libraries containing synonymous, missense, nonsense, and multi-length indel mutations, going from raw reads to final scores and quality control metrics. Raw sequencing data in the form of fastq files is first obtained as demultiplexed paired-end files. The user then defines the experimental architecture using a csv file defining the conditions, replicates, and time points corresponding to each file, which is parsed along with a configuration file. The reads are processed for quality and contaminants using BBDuk, and then the paired reads are error-corrected using BBMerge. The cleaned reads are then mapped onto the reference sequence using BBMap [ 37 ]. Variants in the resulting SAM file are called and counted using the AnalyzeSaturationMutagenesis tool in GATK v4 [ 38 ]. This tool provides a direct count of the number of times each distinct genotype is detected in an experiment. We generate various QC metrics throughout the process and combine them using MultiQC for an easy-to-read final overview [ 39 ].

Due to the degeneracy of indel alignments, the genotyping of codon-level deletions sometimes does not hew to the reading frame due to leftwise alignment. Additionally, due to errors in oligo synthesis, assembly, during in vivo passaging or during sequencing, some genotypes that were not designed as part of the library may be introduced. A fundamental assumption of DMS is the independence of individual variants, and so to reduce noise and eliminate error, our pipeline removes those that were not part of our planned design before analysis, as well as renames variants to be consistent at the amino acid level, before exporting the variant counts in a format for Rosace .

Pre-processing of sequencing count

In a growth DMS screen with V variants, we define v to be the variant index. A function p ( v ) maps the variant v to its position label. T indicates the number of selection rounds and index t is an integer ranging from 0 to T . A total of R replicates are measured, with r as the replicate index. We denote \(c_{v,t,r}\) the raw sequencing count of cells with variant v at time point t in replicate r .

In addition, “mutant” refers to substitution with one of the 20 amino acids, insertion of an amino acid, or deletion. Thus, a variant is uniquely identified by its mutant and the position where the mutant occurs ( p ( v )).

The default pre-processing pipeline of Rosace includes four steps: variant filtering, count imputation, count normalization, and replicate integration. First, variants with more than 50% of missing count data are filtered out in each replicate. Then, variants with a few missing data (less than 50%) are imputed using either the K-nearest neighbor averaging ( K = 10) or filled with 0. Next, imputed raw counts are log-transformed with added pseudo-count 1/2 and normalized by the wild-type cells or the sum of sequencing counts for synonymous mutations. This step, which is proposed by Enrich2 , allows for the computed functional score of wild-type cells to be approximately 0. Additionally, the counts for each variant before selection are aligned to be 0 for simple prior specification of the intercept.

Previous papers suggest the usage of other methods such as total-count normalization when the wild-type is incorrectly estimated or subject to high levels of error [ 18 , 20 ]. We include this in Rosace as an option. Finally, replicates in the same experiment are joined together for the input of the hierarchical model. If a variant is dropped out in some but not all replicates, Rosace imputes the missing replicate data with the mean of the other replicates.

Rosace : hierarchical model and functional score inference

Rosace assumes that the aligned counts are generated by the following time-dependent linear function. Let \(\beta _v\) be the defined functional score or slope, \(b_v\) be the intercept, and \(\epsilon _{g(v)}\) be the error term. The core of Rosace is a linear regression:

where g ( v ) maps the variant v to its mean group—the grouping method will be explained below.

p ( v ) is the function that maps a variant v to its amino acid position. If the information of variants’ mutation types is given, Rosace will assign synonymous variants to many artificial “control” positions. The number of synonymous variants per control position is determined by the maximum number of non-synonymous variants per position. Assigning synonymous variants to control positions incorporates the extra information while not giving too strong a shrinkage to synonymous variants (Additional file 1: Figs. S2-S4). In addition, we regroup positions with fewer than 10 variants together to avoid having too few variants in a position. For example, if the DMS screen has fewer than 10 mutants per position, adjacent positions will be grouped to form one position label. Also, the position of a continuous indel variant is labeled as a mutation of the leftmost amino acid residue (e.g., an insertion between positions 99 and 100 is labeled as position 99 and a deletion of positions 100 through 110 is labeled as position 100).

We assume that the variants at the same position are more likely to share similar functional effects. Thus, we build the layer above \(\beta _v\) using position-level parameters \(\phi _{p(v)}\) and \(\sigma _{p(v)}\) .

The mean and precision parameters are given a weakly informative normal prior and variance parameters are given weakly informative inverse-gamma distribution.

We further cluster the variant into mean groups of 25 based on its value of mean count across time points and replicates. The mapping between the variant and its mean group is denoted as g ( v ). Thus, we model the mean-variance relationship by assuming variants with a lower mean are expected to have higher error terms in the linear regression and vice versa.

Stan [ 40 ] is used in Rosace for Bayesian inference over our model. We use the default inference method, the No-U-Turn sampler (NUTS), a variant of the Hamiltonian Monte Carlo (HMC) algorithm. Compared to other widely used Monte Carlo samplers, for example, the Metropolis-Hastings algorithm, HMC has reduced correlation between successive samples, resulting in fewer samples reaching a similar level of accuracy [ 41 ]. NUTS further improves HMC by automatically determining the number of steps in each iteration of HMC sampling to more efficiently sample from the posterior [ 42 ].

The lower bound of the number of mutants per position index \(|\{v|p(v)=i\}|\) (10) and the size of the variant’s mean group \(g_p\) (25) can be changed.

Rosette : the OCT1 and MET datasets

We use the following datasets as input of the Rosette simulation: the OCT1 dataset by Yee et al. [ 10 ] as an example of positive selection and the MET dataset by Estevam et al . [ 11 ] as an example of negative selection. Specifically, we use replicate 2 of the cytotoxicity selection screen in the OCT1 dataset for both score distribution and raw count dispersion. For the MET dataset, we select the experiment with IL-3 withdrawal under wild-type genetic background (without exon 14 skipping). Raw counts are extracted from replicate 1 but the scores are calculated from all three replicates because of the frequent dropouts at the initial time point.

The sequencing reads and the resulting sequencing counts are processed in the default pipeline described in the previous method sections. Scores are then computed using simple linear regression (the naïve method). The naïve method is used as the Rosette input because we are trying to learn the global distribution of the scores instead of identifying individual variants and, while uncalibrated, naïve estimates are unbiased.

Rosette : summary statistics from real data

Summary statistics inferred by Rosette can be categorized into two types: one for the dispersion of sequencing counts and the other for the dispersion of score distribution.

First, we estimate dispersion \(\eta\) in the sequencing count. We assume the sequencing count at time point 0 reflects the true variant library before selection. Since the functional scores of synonymous variants are approximately 0, the proportion of synonymous mutations in the population should approximately be the same after selection. Let the set of indices of synonymous mutations be \(\textbf{v}_s = \{v_{s1}, v_{s2}, \dots \}\) . The count of each synonymous mutation at time point t is \(\textbf{c}_{\textbf{v}_s, t} = (c_{v_{s1}, t}, c_{v_{s2}, t}, \dots )\) . The model we use to fit \(\eta\) is thus

from which we find the maximum likelihood estimation \(\hat{\eta }\) .

Dispersion of the initial variant library \(\eta _0\) is estimated similarly by fitting a Dirichlet-Multinomial distribution on the sequencing counts of the initial time point assuming that in an ideal experiment, the proportion of each variant in the library should be the same. Similar to above, the indices of all mutations are \(\textbf{v} = \{1, 2, \dots , V\}\) , and the count of each mutation at time point 0 is \(\textbf{c}_{\textbf{v}, 0} = (c_{1, 0}, c_{2, 0}, \dots , c_{V, 0})\) . From the following model

we can again find the maximum likelihood of the variant library dispersion \(\hat{\eta _0}\) . Notice that \(\hat{\eta }_0\) is usually much smaller than \(\hat{\eta }\) (i.e. more overdispersed) because \(\hat{\eta }_0\) contains both the dispersion of the variant library as well as the sequencing step.

To characterize the distribution of functional scores, we first cluster mutants into groups, as mutants often have different properties and exert different influences on protein function. We calculate the empirical Jensen-Shannon divergence (JSD) to measure the distance between two mutants, using bins of 0.1 to find the empirical probability density function. Ideally, a clustering scheme should produce a grouping that reflects the inherent properties of an amino acid that are independent of position. Thus, we are more concerned with the general shape of the distribution than the similarity between paired observations. It leads to our preference for JSD over Euclidean distance as the clustering metric. To cluster mutants into four mutant groups \(g_{m} = \{1, 2, 3, 4\}\) , we use hierarchical clustering (“hclust” function with complete linkage method in R), and we record the proportions \(\widehat{\varvec{p}}\) to simulate any number of mutants in the simulation (the number of mutant groups can also be changed). The underlying assumption is that mutants in each mutant group are very similar and can be treated as interchangeable. We define \(f_1(v)\) as the function that maps a variant to its corresponding mutant group \(g_{m}\) .

Then, we cluster the variants into different variant groups. In the case of our examples, the shape is not unimodal but bimodal. The OCT1 screen has a LOF mode on the right (positive selection) and the MET screen has a LOF mode on the left (negative selection). While it is possible to observe both GOF and LOF variants, we observed in our datasets that GOF variants are so rare that they do not constitute a mode on the mixed distribution, resulting in a bimodal distribution. To cluster the non-synonymous variants into groups \(g_{v}\) , we use the Gaussian Mixture model with two mixtures for our examples to decide the cutoff of the groups, and we fit the Gaussian distribution for each variant group again to learn the parameters of the distribution. The synonymous variants have their own group labeled as control. Let \(f_2(v)\) denote the function that maps a variant to its corresponding variant group \(g_{v}\) . The result of the simulation shows that even the synonymous mutations with scores close to 0 can have large negative effects due to random dropout. Thus, we later set the effect of the control and the neutral group to be constant 0 and still observe a similar distribution as seen in the real data. For each variant, we have one of the models below, depending on whether the variant results in LOF or has no effects:

We use \(\widehat{\varvec{\theta }}\) to denote the collection of estimated distributional parameters for all variant groups.

Finally, we define the number of variants in each variant group at each position

For each position p , we can thus find the count of variants belonging to any mutant-variant group \(\varvec{o}_{p} \in \textbf{N}^{\Vert g_m \Vert \Vert g_v \Vert }\) . Treating each position as an observation, we fit a Dirichlet distribution to characterize the distribution of variant group identities among mutants at any position:

The final summary statistics are \(\hat{\eta }\) , \(\hat{\eta _0}\) , \(\hat{\varvec{p}}\) , \(\hat{\varvec{\theta }}\) , and \(\hat{\varvec{\alpha }}\) . We also need T , the number of selection rounds, to map \(\beta _v\) into the latent functional parameter \(\mu _v\) in growth screens.

Rosette : data generative model

We simulate as the real experiment the same number of mutants M , the number of positions P , and the number of variants V ( \(M \times P\) ). The important hyperparameters that need to be specified are the average number of reads per variant D (100, also referred to as the sequencing depth), initial cell population count \(P_0\) (200 V ), and wild-type doubling rate \(\delta\) between time points ( \(-2\) or 2). One also needs to specify the number of replicates R and selection rounds T .

The simulation largely consists of two major steps: (1) generating latent growth rates \(\mu _v\) and (2) generating cell counts \(N_{v,t,r}\) and sequencing counts \(c_{v,t,r}\) .

In step 1, the mutant group and variant group labeling of each variant is first generated. Specifically, we assign a mutant to the mutant group \(g_m\) by the proportion \(\hat{\varvec{p}}\) and then assign a variant to the variant group \(g_v\) by drawing \(\varvec{o}_p\) from Dirichlet distribution with parameter \(\hat{\varvec{\alpha }}\) (Eq.  10 ). Using \(\hat{\varvec{\theta }}\) , we randomly generate \(\beta _v\) for each variant based on its \(g_v\) (Eq.  8 ). The mapping between \(\beta _v\) and \(\mu _v\) requires an understanding of the generative model, so it will be defined after we present the cell growth model.

In step 2, the starting cell population \(N_{v,r,0}\) is drawn from a Dirichlet-Multinomial distribution using \(\hat{\eta }_0\) and we assume that replicates are biological replicates:

where \(P_0\) is the total cell population. The cells are growing exponentially and we determine the cell count by a Poisson distribution

where \(\Delta t\) is the pseudo-passing time. It differs from index t and will be defined in the next paragraph. Similar to how we define \(\textbf{c}_{\textbf{v}, t, r}\) , we define the true cell count of each variant at time point t and replicate r to be \(\textbf{N}_{\textbf{v}, t, r} = (N_{1, t, r}, \dots , N_{V, t, r})\) . The sequencing count for each variant is

where D is the sequencing depth per variant. Empirically, we can set input \(\hat{\eta }\) and \(\hat{\eta }_0\) slightly higher than the estimated summary statistics. This is because the estimated values encompass all the noises in the experiment, while the true values only represent the noise from the sequencing step.

To find the mapping between \(\beta _v\) and \(\mu _v\) , we define \(\delta\) to be the wild-type doubling rate and naturally compute \(\Delta t:= \frac{\delta \log 2}{\mu _{wt}}\) , the pseudo-passing time in each round. Then we can compute the expectation of \(\beta _v\) with the linear regression model. For simplicity, we omit the replicate index r and assume r is fixed in the next set of equations.

The final mapping between simulated \(\beta _v\) and \(\mu _v\) is then described in the following

with \(\mu _{wt}\) set to be \(\text {sgn}(\delta )\) .

Modified Rosette that favors position-informed models

In the original, position-agnostic version of Rosette , a \(\Vert g_m \Vert \Vert g_v \Vert\) -dimensional vector is drawn from the same Dirichlet distribution for each position. The vector can be regarded as a quota for each mutant-variant group. Variants at each position are assigned their mutant-variant group according to the quota. As a result, at one position, variants from all variant groups (neutral, LOF, and GOF) would exist, and this violates the assumption in Rosace that variants at one position would have similar functional effects (strong LOF and GOF variants are very unlikely to be at the same position). To show that Rosace could indeed take advantage of the position information when it exists in the data, we create a modified version of Rosette where variants at one position could only belong to one variant group. Specifically, a position can have either neutral, LOF, or GOF variants, but not a mixture among any variant groups.

Benchmarking

The naïve method (simple linear regression) is conducted by the “lm” function in R on processed data. For each variant, normalized counts are regressed against time. Raw two-sided p -values are computed from t -statistics given by the “lm” function. It is then corrected using the Benjamini-Hochberg Procedure to adjust the p -values.

For Enrich2 , we use the built-in variant filtering and wild-type (“wt”) normalization. All analyses use a random-effect model as presented in the paper. When there is more than one selection round, we use weighted linear regression. Otherwise, a simple ratio test is performed. The resulting p -values are adjusted using the Benjamini-Hochberg Procedure.

DiMSum requires the variant labeling to be DNA sequences. As a result, we have to generate dummy sequences. It is applied to all simulations with one selection round with the default settings. The z -statistics are computed using the variant’s mean estimate over the estimated standard deviation and the adjusted p -value is computed from the z -score with Benjamini-Hochberg procedure. DiMSum only processes data with one selection round (two time points) and thus may be disadvantaged when analyzing datasets with multiple selection rounds.

mutscan is an end-to-end pipeline that requires the input to be sequencing reads. Conversely, Rosette only generates sequencing counts, which can be calculated from sequencing reads but cannot be used to recover sequencing reads. To facilitate benchmarking, we use a SummarizedExperiment object to feed the Rosette output to their function “calculateRelativeFC,” which does take sequencing counts as input. We benchmark both mutscan(edgeR) and mutscan(limma) with default normalization and hyperparameters as provided in the function. We use the “logFC_shrunk” and “FDR” columns in mutscan(edgeR) output and the “logFC” and “adj.P.Val” columns in mutscan(limma) output.

We run Rosace with position information of variants and labeling of synonymous mutations. However, Rosace is a Bayesian framework so it does not compute FDR like the frequentist methods above. All Rosace power/FDR calculations are done under the Bayesian local false sign rate ( lfsr ) setting [ 28 ]. As a result, in the simulation, we present the rank-FDR curve and the FDR-Sensitivity curve as the metrics instead of setting an identical or different hard threshold on FDR and lfsr . In the real data benchmarking, both the FDR and lfsr thresholds are set to be 0.05.

Rosace without position label is denoted as Rosace (nopos) in the Additional file 1: Figs. S5–S15, S19–S23, and S25. It removes the position layer in Fig.  2 C and keeps only the variant and replicate layer. The test statistics and model evaluation are presented identically as the full Rosace model.

Availability of data and materials

Rosace is implemented as an R package and is distributed on GitHub ( https://github.com/pimentellab/rosace ), under the MIT open-source license. The package also includes functions for Rosette simulation. An archived version of Rosace is available on Zenodo [ 43 ].

The integrated sequencing pipeline for short-read-based experiments is available on GitHub ( https://github.com/odcambc/dumpling ).

Scripts and pre-processed public datasets used to perform data analysis and generate figures for the paper are uploaded on GitHub as well ( https://github.com/roserao/rosace-paper-script ).

The protein datasets we used are as follows: OCT1 [ 10 ], MET [ 11 ], CARD11 [ 14 ], MSH2 [ 12 ], BRCA1 [ 13 ], BRCA1-RING [ 23 ], and Cohesin [ 29 ]. OCT1 and MET are available on NIH NCBI BioProject with accession codes PRJNA980726 and PRJNA993160 . CARD11, BRCA1, and Cohesin are available as supplementary files to their respective publications. MSH2 is available on Gene Expression Omnibus with accession code GSE162130 . BRCA1-RING is available on MaveDB with accession code mavedb:00000003-a-1 .

The benchmarking datasets are EVE [ 31 ] ( evemodel.org ), ClinVar [ 30 ] ( gnomad.broadinstitute.org ), and AlphaMissense [ 32 ] ( alphamissense.hegelab.org ).

Fowler DM, Stephany JJ, Fields S. Measuring the activity of protein variants on a large scale using deep mutational scanning. Nat Protoc. 2014;9(9):2267–84. https://doi.org/10.1038/nprot.2014.153 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Fowler DM, Fields S. Deep mutational scanning: a new style of protein science. Nature Methods. 2014;11(8):801–7. https://doi.org/10.1038/nmeth.3027 .

Araya CL, Fowler DM. Deep mutational scanning: assessing protein function on a massive scale. Trends Biotechnol. 2011;29(9):435–42. https://doi.org/10.1016/j.tibtech.2011.04.003 .

Tabet D, Parikh V, Mali P, Roth FP, Claussnitzer M. Scalable functional assays for the interpretation of human genetic variation. Annu Rev Genet. 2022;56(1):441–65. https://doi.org/10.1146/annurev-genet-072920-032107 .

Article   CAS   PubMed   Google Scholar  

Stein A, Fowler DM, Hartmann-Petersen R, Lindorff-Larsen K. Biophysical and mechanistic models for disease-causing protein variants. Trends Biochem Sci. 2019;44(7):575–88. https://doi.org/10.1016/j.tibs.2019.01.003 .

Romero PA, Tran TM, Abate AR. Dissecting enzyme function with microfluidic-based deep mutational scanning. Proc Natl Acad Sci USA. 2015;112:7159–64. https://doi.org/10.1073/PNAS.1422285112 .

Chen JZ, Fowler DM, Tokuriki N. Comprehensive exploration of the translocation, stability and substrate recognition requirements in vim-2 lactamase. eLife. 2020;9:1–31.

Article   CAS   Google Scholar  

Matreyek KA, Starita LM, Stephany JJ, Martin B, Chiasson MA, Gray VE, et al. Multiplex assessment of protein variant abundance by massively parallel sequencing. Nat Genet. 2018;50(6):874–82. https://doi.org/10.1038/s41588-018-0122-z .

Leander M, Liu Z, Cui Q, Raman S. Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins. eLife. 2022;11. https://doi.org/10.7554/ELIFE.79932 .

Yee SW, Macdonald C, Mitrovic D, Zhou X, Koleske ML, Yang J, et al. The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics. bioRxiv. 2023. https://doi.org/10.1101/2023.06.06.543963 .

Estevam GO, Linossi EM, Macdonald CB, Espinoza CA, Michaud JM, Coyote-Maestas W, et al. Conserved regulatory motifs in the juxtamembrane domain and kinase N-lobe revealed through deep mutational scanning of the MET receptor tyrosine kinase domain. eLife. 2023. https://doi.org/10.7554/elife.91619.1 .

Jia X, Burugula BB, Chen V, Lemons RM, Jayakody S, Maksutova M, et al. Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk. Am J Hum Genet. 2021;108:163–75. https://doi.org/10.1016/J.AJHG.2020.12.003 .

Findlay GM, Daza RM, Martin B, Zhang MD, Leith AP, Gasperini M, et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature. 2018;562(7726):217–22. https://doi.org/10.1038/s41586-018-0461-z .

Meitlis I, Allenspach EJ, Bauman BM, Phan IQ, Dabbah G, Schmitt EG, et al. Multiplexed functional assessment of genetic variants in CARD11. Am J Hum Genet. 2020;107:1029–43. https://doi.org/10.1016/J.AJHG.2020.10.015 .

Flynn JM, Rossouw A, Cote-Hammarlof P, Fragata I, Mavor D, Hollins C III, et al. Comprehensive fitness maps of Hsp90 show widespread environmental dependence. eLife. 2020;9:e53810. https://doi.org/10.7554/eLife.53810 .

Article   PubMed   PubMed Central   Google Scholar  

Steinberg B, Ostermeier M. Shifting fitness and epistatic landscapes reflect trade-offs along an evolutionary pathway. J Mol Biol. 2016;428(13):2730–43. https://doi.org/10.1016/j.jmb.2016.04.033 .

Fowler DM, Araya CL, Fleishman SJ, Kellogg EH, Stephany JJ, Baker D, et al. High-resolution mapping of protein sequence-function relationships. Nat Methods. 2010;7(9):741–6. https://doi.org/10.1038/nmeth.1492 .

Rubin AF, Gelman H, Lucas N, Bajjalieh SM, Papenfuss AT, Speed TP, et al. A statistical framework for analyzing deep mutational scanning data. Genome Biol. 2017;18:1–15. https://doi.org/10.1186/S13059-017-1272-5/FIGURES/7 .

Article   Google Scholar  

Coyote-Maestas W, Nedrud D, He Y, Schmidt D. Determinants of trafficking, conduction, and disease within a K + channel revealed through multiparametric deep mutational scanning. eLife. 2022;11:e76903. https://doi.org/10.7554/eLife.76903 .

Faure AJ, Schmiedel JM, Baeza-Centurion P, Lehner B. DiMSum: An error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies. Genome Biol. 2020;21:1–23. https://doi.org/10.1186/S13059-020-02091-3/TABLES/2 .

Bloom JD. Software for the analysis and visualization of deep mutational scanning data. BMC Bioinformatics. 2015;16:1–13. https://doi.org/10.1186/S12859-015-0590-4/FIGURES/6 .

Bank C, Hietpas RT, Wong A, Bolon DN, Jensen JD. A Bayesian MCMC approach to assess the complete distribution of fitness effects of new mutations: Uncovering the potential for adaptive walks in challenging environments. Genetics. 2014;196:841–52. https://doi.org/10.1534/GENETICS.113.156190/-/DC1 .

Starita LM, Young DL, Islam M, Kitzman JO, Gullingsrud J, Hause RJ, et al. Massively parallel functional analysis of BRCA1 RING domain variants. Genetics. 2015;200(2):413–22. https://doi.org/10.1534/genetics.115.175802 .

Soneson C, Bendel AM, Diss G, Stadler MB. mutscan-a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data. Genome Biol. 2023;12(24):1–22. https://doi.org/10.1186/S13059-023-02967-0/FIGURES/6 .

Eddy SR. Accelerated Profile HMM Searches. PLOS Comput Biol. 2011;7(10):1–16. https://doi.org/10.1371/journal.pcbi.1002195 .

Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2009;26(1):139–40. https://doi.org/10.1093/bioinformatics/btp616 .

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.

Stephens M. False discovery rates: a new deal. Biostatistics. 2017;18:275–94. https://doi.org/10.1093/BIOSTATISTICS/KXW041 .

Article   PubMed   Google Scholar  

Kowalsky CA, Whitehead TA. Determination of binding affinity upon mutation for type I dockerin-cohesin complexes from C lostridium thermocellum and C lostridium cellulolyticum using deep sequencing. Proteins Struct Funct Bioinforma. 2016;84(12):1914–28.

Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46(D1):D1062–7.

Frazer J, Notin P, Dias M, Gomez A, Min JK, Brock K, et al. Disease variant prediction with deep generative models of evolutionary data. Nature. 2021;599(7883):91–5.

Cheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, Applebaum T, et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science. 2023;381(6664):eadg7492.

Starr TN, Greaney AJ, Hilton SK, Ellis D, Crawford KHD, Dingens AS, et al. Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding. Cell. 2020;182:1295-1310.e20. https://doi.org/10.1016/J.CELL.2020.08.012 .

Stiffler M, Hekstra D, Ranganathan R. Evolvability as a function of purifying selection in TEM-1 beta-lactamase. Cell. 2015;160(5):882–892. Publisher Copyright: © 2015 Elsevier Inc. https://doi.org/10.1016/j.cell.2015.01.035 .

Faure AJ, Domingo J, Schmiedel JM, Hidalgo-Carcedo C, Diss G, Lehner B. Mapping the energetic and allosteric landscapes of protein binding domains. Nature. 2022;604(7904):175–83. https://doi.org/10.1038/s41586-022-04586-4 .

Mölder F, Jablonski KP, Letcher B, Hall MB, Tomkins-Tinch CH, Sochat V, et al. Sustainable data analysis with Snakemake. F1000Research. 2021;10:33.  https://f1000research.com/articles/10-33/v2 .

Bushnell B. BBTools software package. 2014. https://sourceforge.net/projects/bbmap . Accessed 11 June 2021.

Van der Auwera GA, O’Connor BD. Genomics in the cloud: using Docker, GATK, and WDL in Terra. Sebastopol: O’Reilly Media; 2020.

Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8. https://doi.org/10.1093/bioinformatics/btw354 .

Stan Development Team. RStan: the R interface to Stan. 2023. R package version 2.21.8. https://mc-stan.org/ . Accessed 22 May 2024.

Betancourt M. A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint arXiv:1701.02434. 2017.  https://arxiv.org/abs/1701.02434 .

Hoffman MD, Gelman A. The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J Mach Learn Res. 2014;15(47):1593–623.

Google Scholar  

Rao J. pimentellab/rosace. 2023. Zenodo. https://doi.org/10.5281/zenodo.10814911 .

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Andrew Cosgrove was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Ruiqi Xin and Christian Macdonald contributed equally to this work.

Authors and Affiliations

Department of Computer Science, UCLA, Los Angeles, CA, USA

Jingyou Rao & Harold Pimentel

Computational and Systems Biology Interdepartmental Program, UCLA, Los Angeles, CA, USA

Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA

Christian Macdonald, Matthew K. Howard, Gabriella O. Estevam, Sook Wah Yee, James S. Fraser & Willow Coyote-Maestas

Tetrad Graduate Program, UCSF, San Francisco, CA, USA

Matthew K. Howard & Gabriella O. Estevam

Department of Pharmaceutical Chemistry, UCSF, San Francisco, CA, USA

Matthew K. Howard

Department of Mathematics, Baruch College, CUNY, New York, NY, USA

Mingsen Wang

Quantitative Biosciences Institute, UCSF, San Francisco, CA, USA

James S. Fraser & Willow Coyote-Maestas

Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

Harold Pimentel

Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

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JR, CM, WCM, and HP jointly conceived the project. JR and HP developed the statistical model and the simulation framework. JR, MW, and RX wrote the software and its support. JR performed the data analysis and benchmarking. CM wrote the sequencing pipeline. SWY and CM performed the OCT1 experiment and GOE performed the MET experiment. JR and HP wrote the manuscript with input from MW, CM, WCM, MH, and JSF. All authors read and approved the final manuscript.

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Correspondence to Willow Coyote-Maestas or Harold Pimentel .

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Competing interests

JSF has consulted for Octant Bio, a company that develops multiplexed assays of variant effects. The other authors declare that they have no competing interests.

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Rao, J., Xin, R., Macdonald, C. et al. Rosace : a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage. Genome Biol 25 , 138 (2024). https://doi.org/10.1186/s13059-024-03279-7

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Received : 31 October 2023

Accepted : 14 May 2024

Published : 24 May 2024

DOI : https://doi.org/10.1186/s13059-024-03279-7

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