research hypothesis proposes

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

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 .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, 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 types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

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 .

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

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 ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize 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.

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

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.

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 .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Prevent plagiarism. Run a free check.

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).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved June 11, 2024, from https://www.scribbr.com/methodology/hypothesis/

Is this article helpful?

Shona McCombes

Other students also liked, construct validity | definition, types, & examples, what is a conceptual framework | tips & examples, operationalization | a guide with examples, pros & cons, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Educational resources and simple solutions for your research journey

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

Researcher.Life is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Researcher.Life All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 21+ years of experience in academia, Researcher.Life All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $17 a month !    

Related Posts

Tips on Writing the Acknowledgments Section 

What is Confidence Interval and How to Calculate it (with Examples)

• Resources Research Proposals --> Industrial Updates Webinar - Research Meet
• Countries-Served
• Add-on-services

Text particle

feel free to change the value of the variable "message"

How to Write a Research Hypothesis?

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 the 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).

Example hypothesis from Question to Statements

• Question: Are health and mental stress-related?
• Statement: I predict that health and mental stress are related

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables.

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables, extraneous variables, or confounding variables, be sure to jot those down as you go to minimize the chances that research bias will affect your results.

Example: Daily exposure to the sun leads to increased levels of happiness.

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.

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).

Types of Hypotheses

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 that there is a relationship between one independent variable and one dependent variable.

Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.

Null hypothesis : The null hypothesis states that the two variables under investigation have no relationship which means one variable does not affect the other variable. It claims that the findings are purely coincidental and have no presence in the validity of the hypothesis being investigated.

Alternative hypothesis : The alternative hypothesis states the effect of a relationship between one variable to another variable. In this, the result of the study is not due to the chance of occurrence in the study. Also, we accept the alternative hypothesis if the null hypothesis is denied. We do not accept the alternative hypothesis if the null hypothesis is not rejected.

Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.

Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

Essential 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 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?

Developing a hypothesis 

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 ensure that you’re embarking on a relevant topic. This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize 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 H0, while the alternative hypothesis is H1 or Ha.

https://www.scribbr.com/methodology/hypothesis/#:~:text=about%20writing%20hypotheses-,What%20is%20a%20hypothesis%3F,aspects%20of%20your%20research%20question.

https://www.verywellmind.com/what-is-a-hypothesis-2795239

https://www.enago.com/academy/how-to-develop-a-good-research-hypothesis/

https://gradcoach.com/what-is-a-research-hypothesis-or-scientific-hypothesis/

https://www.ilovephd.com/what-is-research-hypothesis/#:~:text=A%20research%20hypothesis%20is%20an,a%20precise%20and%20testable%20statement.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

How to Propose a Formal Hypothesis

Sara mahuron.

Students begin learning the basics of scientific research at a young age helping to prepare them for the day when they will be asked to form their own hypothesis for research. While this is realized for people differently and at different levels, the basic process remains the same. A hypothesis is proposed as a testable statement someone wishes to research. The significance of research papers, whether an informal paper, or a student's college thesis or dissertation, are often at the mercy of the hypothesis. This makes proposing the formal hypothesis statement an important part of the overall research project.

Conduct a literature review on the topic you are interested in researching. A hypothesis needs to be theoretically grounded in existing research unless you are researching something for which there is not existing research. A literature review should be comprehensive and include an analysis of varying conclusion and research findings related to your topic.

Write down questions or correlations you find in the literature review that interest you in research. A hypothesis requires a proposed relationship between two variables. Informal questions or correlations can be the basis for a hypothesis. A hypothesis is a statement that predicts a relationship exists or doesn't exist.

Identify the two variables in your question. Label which one is the independent variable and which one is the dependent variable. The independent variable must cause some change in the dependent variable. For example, if you are interested in studying the relationship between soil nutrients and plants, you might propose that your independent variable -- soil nutrients -- causes your dependent variable -- plant growth -- to either grow better or worse. The direction of the relationship between the two variables is determined by which variable is independent and which one is dependent.

Write your hypothesis statement. The statement is a prediction of what you think will happen between the variables. This statement needs to be clear and concise, and written in a fashion that can be tested. The two common types of hypothesis statements are the null hypothesis and the alternative hypothesis. The null hypothesis is used when no relationship is expected. An example of a null hypothesis is, "There is no difference in plant growth between those that do and do not receive soil nutrients." The alternative hypothesis is used when a relationship is expected. An example of an alternative hypothesis is, "Plants who receive soil nutrients will grow better than those that do not."

Ask your peers and faculty for feedback on your hypothesis statement. Make sure it is clearly communicated to others, and make any corrections you feel are warranted after reviewing the feedback.

• 1 Emory University; The Elements of a Proposal; Frank Pajares
• 2 University of Wisconsin-Eau Claire: Generating a Research Proposal

About the Author

Sara Mahuron specializes in adult/higher education, parenting, budget travel and personal finance. She earned an M.S. in adult/organizational learning and leadership, as well as an Ed.S. in educational leadership, both from the University of Idaho. Mahuron also holds a B.S. in psychology and a B.A. in international studies-business and economics.

Related Articles

How to Make a Hypothesis Statement

What Is a Problem Statement in a Lab Report?

What Is a Conditional Statement in Math?

How to Write a Discussion for an APA Style Paper

How to Evaluate a Research Design

Methods of Research Design

How to Interpret the Shapiro-Wilk Test

Science Project for Third Graders About Density of...

How to Teach the Scientific Method to Children

Can Double Replacement Reactions Have Two Insoluble...

What Is the Chemical Formula for Magnesium Nitrate...

How to Write a Problem Statement for a Science Project

Do Congressmen Have to Be Naturally Born Citizens?

Correlational Methods vs. Experimental Methods

How to Develop a Problem Statement

What Are the Dangers of Staying in an Unhealthy Relationship...

Differences Between Experimental & Descriptive Research

How to Write Up an Elementary Volcano Science Project

Define Primary & Secondary Data

Which States Use the Caucus System?

Regardless of how old we are, we never stop learning. Classroom is the educational resource for people of all ages. Whether you’re studying times tables or applying to college, Classroom has the answers.

• Accessibility
• Terms of Use
• Privacy Policy
• Copyright Policy
• Manage Preferences

© 2020 Leaf Group Ltd. / Leaf Group Media, All Rights Reserved. Based on the Word Net lexical database for the English Language. See disclaimer .

• More from M-W
• To save this word, you'll need to log in. Log In

Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• counter - hypothesis
• nebular hypothesis
• null hypothesis
• planetesimal hypothesis
• Whorfian hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

Dictionary Entries Near hypothesis

hypothermia

hypothesize

Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 11 Jun. 2024.

Kids Definition

Kids definition of hypothesis, medical definition, medical definition of hypothesis, more from merriam-webster on hypothesis.

Nglish: Translation of hypothesis for Spanish Speakers

Britannica English: Translation of hypothesis for Arabic Speakers

Britannica.com: Encyclopedia article about hypothesis

Subscribe to America's largest dictionary and get thousands more definitions and advanced search—ad free!

Can you solve 4 words at once?

Word of the day.

See Definitions and Examples »

Get Word of the Day daily email!

Popular in Grammar & Usage

What's the difference between 'fascism' and 'socialism', more commonly misspelled words, commonly misspelled words, how to use em dashes (—), en dashes (–) , and hyphens (-), absent letters that are heard anyway, popular in wordplay, the words of the week - june 7, 8 words for lesser-known musical instruments, 9 superb owl words, 10 words for lesser-known games and sports, your favorite band is in the dictionary, games & quizzes.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Biomed Semantics
• v.2(Suppl 2); 2011

Representation of research hypotheses

Larisa n soldatova.

1 Department of Computer Science, Penglais, Aberystwyth University, Wales, UK

Andrey Rzhetsky

2 Department of Medicine & Department of Human Genetics, the University of Chicago, USA

Hypotheses are now being automatically produced on an industrial scale by computers in biology, e.g. the annotation of a genome is essentially a large set of hypotheses generated by sequence similarity programs; and robot scientists enable the full automation of a scientific investigation, including generation and testing of research hypotheses.

This paper proposes a logically defined way for recording automatically generated hypotheses in machine amenable way. The proposed formalism allows the description of complete hypotheses sets as specified input and output for scientific investigations. The formalism supports the decomposition of research hypotheses into more specialised hypotheses if that is required by an application. Hypotheses are represented in an operational way – it is possible to design an experiment to test them. The explicit formal description of research hypotheses promotes the explicit formal description of the results and conclusions of an investigation. The paper also proposes a framework for automated hypotheses generation. We demonstrate how the key components of the proposed framework are implemented in the Robot Scientist “Adam”.

Conclusions

A formal representation of automatically generated research hypotheses can help to improve the way humans produce, record, and validate research hypotheses.

Availability

http://www.aber.ac.uk/en/cs/research/cb/projects/robotscientist/results/

Research hypotheses are the heart of scientific endeavours; the accurate, unambiguous and operational representation of them is vital for the formal recording and analysis of investigations. Hypotheses should be represented and recorded so as to accurately capture the semantic meaning of the hypothesis and to promote the manual (or automated) design of experiments to test these hypotheses.

A number of projects aim to address the need to represent and record research hypotheses in a semantically defined form. Hypotheses in the Semantic Web Applications in Neuroscience (SWAN) Alzheimer knowledge-base are portions of natural language text which are represented as research statements (discourse-elements), and these are linked (via discourse-relations) to other discourse elements and citations which specify the author's name, article, journal, etc. [ 1 ]. Similarly, the Ontology for Biomedical Investigations (OBI) models hypotheses as the class obi:hypothesis textual entity , (here and further in the text we use italic for ontological classes and relations where appropriate), where hypotheses are part of obi:objective specification of obi:investigation [ 2 , 3 ]. The ART project [ 4 ] considers scientific papers as textual representation of scientific investigations, and uses the key classes from the generic ontology of experiments EXPO [ 5 ] to annotate papers. The class expo:hypothesis is used to annotate sentences which describe research hypotheses. For example, the paper b310850 from the ART Corpus of 225 annotated by experts papers [ 6 ] contains a sentence which has been annotated as a hypothesis:

<s sid="41"><annotationART atype="GSC" type="Hyp" conceptID="Hyp1" novelty="None" advantage="None">This means that whereas a central ligand may change chemical properties somewhat, this should only be a second order effect on the properties we are studying here.</annotationART></s>

The extraction of hypotheses from literature as textual entities, and the deposition of these hypotheses into publicly available, comprehensive, and semantically annotated collections opens up new prospects for knowledge sharing and exchange. The open and easy access to a whole range of alternative hypotheses reflecting a plurality of often contrarian theories, opinions, and views could significantly speed up the scientific progress. Unfortunately, it is typically hard to capture the precise semantic meaning of a hypothesis expressed as a textual entity; as sometimes it is impossible to understand the meaning and correctly process the hypothesis without reading a considerable portion of the surrounding text. Textual representation of the hypotheses retrieved from literature is mostly intended for “consumption” by humans, and has limited value for automatic processing.

A number of projects try to overcome this limitation and translate hypotheses into a machine-processable format. The HyBrow (Hypothesis Browser) tool for designing hypotheses, and evaluating them for consistency with existing knowledge, uses an ontology of hypotheses to represent hypotheses in machine understandable form as relations between objects (agents) and processes [ 7 , 8 ]. A hypothesis event is considered to be an abstract biological event. The ontology accommodates currently available literature data, extracted primarily from Yeast Proteome Database at a coarse level of resolution [ 9 ]. The Large-Scale Discovery of Scientific Hypotheses project aims to collect and make visible, comparable, and computable contrarian (with respect to a standard paradigm) hypotheses produced by the communities focusing on three classes of disease phenotypes (cancer, neuropsychiatric and infectious disorders) [ 10 ]. In this project hypotheses and supporting evidences are collected and structured in the form of statements, and then formalised as a propositional graph.

It is now likely that the majority of hypotheses in biology are computer generated. Computers are increasingly automating the process of hypothesis formation, for example: machine learning programs (based on induction) are used in chemistry to help design drugs; and in biology, genome annotation is essentially a vast process of (abductive) hypothesis formation. Such computer-generated hypotheses have been necessarily expressed in a computationally amenable way, but it is still not common practice to deposit them into a public database and make them available for processing by other applications.

In this paper, we extend the representation of hypotheses as textual entities to the representation of hypotheses, which are automatically generated by a machine, as logical entities following the HyBrow approach. This approach is also consistent with the representation of hypotheses by the Large-Scale Discovery of Scientific Hypotheses project. The proposed representation of research hypotheses is based on LABORS (the LABoratory Ontology for Robot Scientists) [11, Suppl.], and the representation of structural research units is based on LABORS and DDI (an ontology for the Description of Drug Discovery investigations) [ 12 ]). Instances of the hypotheses defined in LABORS, and instances of the recorded research units, are stored in a publicly available database [ 11 , 13 ]. All the hypotheses discussed below have been automatically generated by the Robot Scientist “Adam” (A Discovery Machine) [ 14 ]. An explicit semantically defined representation of hypotheses enables improvements in the representation of investigations designed to test those hypotheses, and in the consistency and validity checking of the conclusions about those hypotheses within the investigations.

The main contribution of this paper is a formal representation of research hypotheses in a logically defined form which enables scientists (robots or humans) to capture the precise semantic meaning of the hypothesis statements, and also promotes the design of experiment to test these hypotheses. The proposed formalism supports the decomposition of a generic hypothesis into specific hypotheses, and the representation of hypotheses as members of an exhaustive set of hypotheses covering a specific domain. The paper also proposes a framework for automated hypotheses generation, and the key components of the proposed framework are implemented for Adam. The authors discuss some of the limitations of the “conventional realism” in biomedicine for the formalisation of research hypotheses. An extension of the proposed approach, the probabilistic representation of research hypotheses, is also discussed. Our experiences in formally recording research hypotheses, and analysing automatically generated hypotheses are summarised as “lessons learned”. The examples in this paper are from the investigations run by Adam, the investigation into re-discovery of gene functions in aromatic amino acids pathway, and the investigation into novel biological knowledge, which are reported in [ 14 ] and [ 11 ]; all the information about the investigations, including procedures and data, is available at the Robot Scientist project web site [ 13 ].

We have developed the Robot Scientist “Adam” with the intended application domain of Systems Biology and Functional Genomics. The idea of a Robot Scientist is to combine laboratory automation, automated hypothesis formation, and other techniques from Artificial Intelligence to “close the loop” and automate the whole scientific process (see Figure ​ Figure1) 1 ) [ 11 ]. Adam has a -20°C freezer, 3 incubators, 2 readers, 3 liquid handlers, 3 robotic arms, 2 robot tracks, a centrifuge, a washer, an environmental control system, etc. It is capable of initiating ~1,000 new experiments and >200,000 observations per day in a continuous cycle. The representations proposed in this paper have been tested on Adam.

A concept of a Robot Scientist. A Robot Scientist is a physically implemented system which is capable of running cycles of scientific experimentation in a fully automatic manner: hypothesis formation, experiment selection, experiment execution, and results interpretation. The Robot Scientist system uses initial background knowledge and outputs new or updated knowledge.

The proposed representation of research and negative hypotheses and its logical and textual representations are defined and tested within LABORS [ 11 , 15 ]. LABORS is designed to support investigations run by Adam for the area of Systems Biology and Functional Genomics. Thousands of experiments and corresponding hypotheses have been successfully recorded and re-used for further experimentation on the basis of LABORS. LABORS uses EXPO as an upper level ontology [ 5 ], and RO as a set of relations [ 16 ]. LABORS is expressed in W3C Ontology Web Language OWL-DL.

The modelling of structural research units is based on both LABORS and DDI. DDI was designed to support investigations run by the Robot Scientist “Eve” for the area of drug discovery [ 12 ], and developed as an application of OBI. As a consequence, DDI uses BFO (Basic Formal Ontology) as an upper level ontology. Several compromise solutions were made within DDI in order to fit into the BFO framework. For example, the class ddi:hypothesis is defined as the subclass of the class iao:information content entity. DDI has been recently submitted to OBO, and negotiations about possible compromises to adjust for different representations are in progress.

Both LABORS and the corresponding database representations have been translated into Datalog in order to enable reasoning with the use of SWI-Prolog engine. The is-a and instance-of hierarchy has been translated into datalog with the use of one-ary predicates:

classA(subclassB).

classA(instance-ofC).

Other triplets have been translated into datalog with the use of bi-ary predicates:

Relation(classA,classB).

where a relation is a defined in LABORS or an additional predicate.

Automatic generation of hypotheses

The Robot Scientist project is driven by the technological necessity to increase hypotheses production throughput (see Figure ​ Figure1). 1 ). Biological data are now produced at an industrial scale, while data analysis, and especially hypotheses formation, often remains manual. There is still strong belief that only human intelligence is capable of production of research hypotheses. The Robot Scientist project has proved that a machine can not only automatically generate scientifically valuable hypotheses, but also test them and make conclusions about their validity [ 14 ].

The nature of scientific discovery necessitates a succession of scientific theories: older dominant theories (paradigms) are contradicted by new experimental evidence, new paradigms are introduced, etc. [ 18 ]. The majority of discoveries in biomedicine are factual, e.g. gene G has function A, drug D can cure disease E, etc. The discovery of such scientific knowledge is based on abductive and deductive inferences, and modern technology is now able to automate the inference of possible new facts and their experimental confirmation [ 14 ]. The techniques for inductive inference are also in place, e.g. Inductive Logic Programming, but the results are still rather modest [ 17 ].

We argue that the automated formation of hypotheses requires the following key components:

1. Machine–computable representation of the domain knowledge.

2. Abductive or inductive inference of novel hypotheses.

3. An algorithm for the selection of hypotheses.

4. Deduction of the experimental consequences of hypotheses.

Adam has been designed to fully automate yeast growth experiments, and we show below how the key components of its hypothesis generation are implemented. The automated formation of hypotheses by Adam includes the following components:

1. Yeast metabolic model which encodes the background knowledge about yeast functional genomics domain. Our group has developed a logical formalism for modeling metabolic pathways (encoded in Prolog) [ 2 ]. This is essentially a directed graph with metabolites as nodes and enzymes as arcs. If a path can be found from cell inputs (metabolites in the growth medium) to all the cell outputs (essential compounds), then the cell can grow.

2. Abductive Logic Programming for the inference of missing arcs/labels in the yeast metabolic graph. Adam abductively hypothesizes new facts about yeast functional biology by inferring what is missing from a model. In our original work on robot scientists, we used a purely logical approach to hypothesis formation based on applying abductive logic programming to a logical model of a yeast metabolism subset [ 14 ]. Unfortunately, this general method is too inefficient to deal with large bioinformatic models. We therefore developed an alternative approach based on using standard bioinformatic methods – these are essentially based on abductive hypothesis formations [ 11 ]. Adam uses an automated strategy based on 1) finding the enzyme class (EC number) of the missing reaction, 2) finding genes that code for this EC class in other organisms, 3) finding homologous genes in yeast.

3. The procedure for selection of hypotheses which aims to satisfy the following combination of the selection criteria:

• it should encapsulate the maximum of information about a domain of interest;

• it should possess the maximum prior probability of being correct;

• it would require the minimum cost to test.

Adam investigates a finite hypothesis space, and uses a Bayesian approach that puts prior probabilities on the hypotheses. These priors have the potential to incorporate the complexity of the hypotheses [ 14 ].

4. The deduction of experimental outputs . Adam follows a hypothetico-deductive methodology. Adam abductively hypothesizes new facts about yeast functional biology, then it deduces the experimental consequences of these facts using its model of metabolism, which it then experimentally tests. To select experiments Adam takes into account the variable cost of experiments, and the different probabilities of hypotheses [ 14 ]. Adam chooses its experiments to minimise the expected cost of eliminating all but one hypothesis. This is in general a NP complete problem and Adam uses heuristics to find a solution. LABORS defines the class labors:expected output to model Adam's predictions for experiment results.

Representation of automatically generated hypotheses

The class labors:hypothesis is defined as the subclass of the class labors:proposition which is equivalent to the class iao:information content entity. Whilst, LABORS has been designed to support automated investigations run by robots and therefore it does not have textual definitions, a sister DDI ontology for the Robot Scientist “Eve” provides a textual definition for the class hypothesis: “information content entity that is an assertion which is intended to be tested” [ 12 ]. The classes labors:research hypothesis and labors:negative hypothesis are defined as the subclasses of the class labors:hypothesis. The class labors:hypothesis is linked via the relation has-representation to the class labors:representation which has the subclasses labors:textual representation and labors:logical representation.

Specification of hypotheses into different levels of granularity

The automated investigations of robot scientist are generally complex, involving multiple study domains, and different levels of granularity. For example, the investigation into automation of science, in which Adam discovered novel knowledge about yeast genes, involves four different domains, and has 10 levels (see Figure ​ Figure2) 2 ) [ 11 ]. The levels are defined by a number of features including the corresponding hypotheses. On the top level is the hypothesis that it is possible to fully automate scientific discovery. This hypothesis is further decomposed into more specialised hypotheses, e.g. is it possible to automatically re-discover biological knowledge, that manual experiments would confirm the results obtained automatically by the robot, etc. At the lowest level of the investigation are hypotheses about quantitative yeast growth rates which are linked to the experimental data – optical density (OD) readings. The classes labors:pregrowth optical data reading and labors:growth optical data are modelled as subclasses of the class labors:optical data reading which is a subclass of the class labors:experiment observation . Complicated logical inferences are required in order to make the conclusion that scientific discovery can be fully automated from the basis of a large number of ODs (the inference procedures are available at the Robot Scientist project website [ 13 ]). The representation of hypotheses plays an essential role in the logical representation of such complex investigations . A machine can operate with hypotheses if and only if they are represented in a machine operable form.

Levels of investigations run by Adam. An example of the levels of the representation of the investigation executed by the Robot Scientist “Adam” (a fragment). The relations are has part .

LABORS enables the recording and storage in a relational database of the instances of the classes labors:logical representation which are linked to the instances of the classes labors:research hypothesis (H 0 ) and labors:negative hypothesis (H 1 ) [ 11 ]. The robot operates in a “closed world”, where a finite number of reactions, metabolites, and yeast strains are present. Therefore, the logical negation of hypotheses is possible. However, ontological representations utilise the “open world assumption” (OWA), where nothing outside of the ontologically defined collection of facts is known to be true or false. Relational databases operate under the “closed world assumption” (CWA), where everything outside the stated facts is false. In order to enable reasoning about the Adam's world over orthogonal data and knowledge representations, namely ontology, database, and models in Prolog, we chose to explicitly define negative hypotheses instead of inferring them.

Let us consider the decomposition of hypotheses into more specialized ones in more detail (see Figure ​ Figure3). 3 ). Adam with the use of its background knowledge and bioinformatic facts, generates hypotheses about yeast genes and enzymes, i.e. gene YER152C encodes an enzyme with the enzyme class E.C.2.6.1.39 (the inference procedures are available at the Robot Scientist project website [ 13 , 20 ]) . The research hypotheses are encoded in the logical programming language Prolog, e.g.

Examples of hypotheses generated by Adam . Each of the research and negative hypotheses from the hypotheses set of the study level is derived into more specialised hypotheses which are members of the hypotheses set of the cycle of study level. The hypotheses have logical and corresponding textual representations.

encodes(yer152c,ec.2.6.1.39).

The enzyme class E. C. 2.6.1.39 is that of 2-aminoadipate transaminases. Adam also explicitly records the corresponding negative (null) hypotheses being tested:

not_encodes(yer152c,ec.2.6.1.39).

The research and negative hypotheses encoded in Prolog are stored in a relational database as instances of the class labors:logical representation . A logical representation of hypotheses is used to communicate with modules of Adam's software. For the convenience of humans, research hypotheses can be also translated into natural language text, i.e.:

gene YER152C encodes an enzyme with enzyme class E.C.2.6.1.39.

This is defined in LABORS as an instance of the class labors:textual representation .

Adam used abduction to form hypotheses. A real physical experiment is generally required to confirm (or to increase the probability) that a hypothesis is correct. However, such entities as the gene YER152C and an enzyme with the enzyme class E.C.2.6.1.39 exist only in Adam's memory, and not in Adam's physical world. In the real physical world Adam can operate only with yeast strains and metabolites. The hypothesis that gene YER152C encodes an enzyme with the enzyme class E.C.2.6.1.39 therefore has to be specialized to such a level that the robot can physically test the hypothesis. Using its background knowledge, Adam infers that if the research hypothesis is correct, then the addition of the following metabolites with the KEGG numbers {"type":"entrez-nucleotide","attrs":{"text":"C00047","term_id":"1432277","term_text":"C00047"}} C00047 , {"type":"entrez-nucleotide","attrs":{"text":"C00449","term_id":"55826153","term_text":"C00449"}} C00449 , and {"type":"entrez-nucleotide","attrs":{"text":"C00956","term_id":"1433186"}} C00956 , correspondingly, to growth medium for a yeast strain with a removed gene YER152C would restore the yeast growth rate (see Figure ​ Figure3 3 and [ 13 ] for the inference procedures):

affects_growth(c00047,delta YER152C).

affects_growth(c00449,delta YER152C).

affects_growth(c00956,delta YER152C).

An example of the text representation of these new hypotheses is:

addition of the metabolite lysine ( {"type":"entrez-nucleotide","attrs":{"text":"C00449","term_id":"55826153","term_text":"C00449"}} C00449 ) to a standard growth medium will differentially affect the growth of the yeast strain delta_YER152C compared to the wild type (Mat A, BBY4741).

If the metabolites are available, then using the yeast strain YER152C from its yeast strains library, Adam can physically test the hypotheses above. Adam designs microtitre plate layouts with controls and replicates in order to collect enough statistics to accurately analyse the results and runs the experiments. The class labors:plate layout is defined as the subclass of the class labors:design . In a series of experiments Adam tries to decide whether the difference in growth rate of two strains is significantly different and whether this difference can be attributed to differences in experimental conditions. In each case Adam compares four experimental setups: (1) a yeast strain with specific gene deleted and growing on a defined medium, (2) the same strain growing on the defined medium with a metabolite added, (3) wild type (WT) strain growing on the defined medium, and (4) WT strain growing on the defined medium and the metabolite. These experimental setups are combined within labors:trial . To make this decision, Adam uses decision trees and random forests combined with re-sampling methods. The deletion strains are mutant versions with genes removed that hypothesized to encode an orphan enzyme. Adam uses standard 96-well plates to grow the yeast, which enabled 24 repeats of each strain and medium combination. To control for intra-plate environmental effects, Adam uses labors:latin squares strategy of experiment design which is defined as the subclass of the class labors:normalization strategy .

The results of the study are represented with the use of the same terms that were employed to encode the hypotheses (see Figure ​ Figure4 4 ):

Examples of results and conclusions produced by Adam . Each of the research and negative hypotheses from the hypotheses set of the hypotheses set of the cycle of study level has been tested, observations analysed, decision procedures invoked and conclusions have been made. The results are expressed with the use of the same terms as the corresponding hypotheses. The results have logical and corresponding textual representations. Conclusions are made on the basis of the results with the use of decision procedures.

not_affects_growth(c00449,delta YER152C).

The corresponding textual representation of the result is:

addition of the metabolite lysine ( {"type":"entrez-nucleotide","attrs":{"text":"C00449","term_id":"55826153","term_text":"C00449"}} C00449 ) to a standard growth medium differentially does not affect the growth of the yeast strain delta_YER152C compared to the wild type (Mat A, BBY4741).

The corresponding labors:conclusion or interpretations of the experiment results are expressed in the following form:

hypothesis X has been confirmed.

hypothesis X has been denied.

The conclusions are made following the corresponding decision procedures on the basis of the results (see the procedures at [ 13 ]). A more generic conclusion is made on the basis of more specific conclusions that correspond to more specific hypotheses. For example, a conclusion that a generic hypothesis is confirmed may be made if two out of three more specific hypotheses have been confirmed.

LABORS supports explicit and unambiguous recording of not only observations (i.e. ODs) (which is commonly done), but also the experimental results (i.e. predicate(metabolite,yeast_strain)), the corresponding conclusions (i.e. hypothesis X has been confirmed), and decision procedures employed for making those conclusions. The classes labors:experiment observation , labors:result , labors:conclusion are subclasses of the class labors:research outcome .

If hypotheses and conclusions of an investigation are recorded in this way, then it is possible to check how exactly each conclusion has been made: on what basis, and following what assumptions. If everything is explicitly recorded, then it is objectively possible to check which procedures were used, if the conclusions are valid, if they correspond to the stated hypotheses or those hypotheses have been replaced by related but different ones, etc. We argue that in the future all scientific investigations will be (or, at least, should be) recorded and reported in a similar way to enable complete consistency and validity checking of the results - these checks could potentially be done automatically.

Sets of hypotheses for cyclic investigations

Robots can potentially generate thousands of hypotheses and test them in parallel. However, even for robots it is generally not practical to exhaustively test all possible hypotheses as hypothesis spaces can be very large. Adam selects hypotheses and designs experiments to test them following the combination of the selection criteria described in the previous sections. Such selected hypotheses are not completely independent, and LABORS models them as the class labors:hypotheses set (the subclass of the class labors:collection ) where each particular hypothesis is a member of the set. A set of hypotheses is tested in cycles. Each cycle of investigation has a specified input labors:hypotheses set . Adam designs and runs experiments to test each hypothesis from the set. Adam then analyses the results of the experiments, and makes conclusions about whether a particular hypothesis has been confirmed or rejected. The rejected hypotheses are eliminated from the input labors:hypotheses set and the remaining set of hypotheses are considered as a specified output of the current labors:cycle of study . Adam updates its current model of metabolism and generates a new set of hypotheses, where the rejected on previous cycles hypotheses are excluded. This labors:hypotheses set is considered as a specified input for the next labors:cycle of study . Adam continues to run cycles of studies until the labors:hypotheses set contains only one hypothesis or the robot runs out of resources [ 14 ]. In the event that all hypotheses are eliminated a backtracking procedure is invoked [ 11 ]. If all hypotheses are eliminated, then the correct hypothesis, which is known a priori to be in the set, has been rejected. This can occur because Adam's observations are noisy. In such a case a backtracking procedure does more experiments to try to decide which hypothesis has been wrongly eliminated.

The analysis of the research hypotheses which were produced within Adam's investigations enabled us to improve the logical representation of the structural units of general scientific investigations by introducing new research units: trial , study , cycle of study , and replicate (see the next section and [ 12 ] for more detail) .

Restrictions in the ontological representation of scientific discourse elements

Obi limitations.

Currently prevalent ontological representations are not sufficient for the recording of hypotheses sets and complex (particularly cyclic) investigations. The most advanced project with the aim to support formal description of scientific investigations is OBI [ 3 , 21 ]. OBI aims to support the detailed description of investigations from the whole area of biomedicine. OBI descriptors include all phases of the investigation process, such as planning, execution and reporting, information and material entities that participate in these processes, as well as roles and functions. OBI intends to serve as the standard for the recording of biomedical investigations.

OBI represents a state-of-the-art for a cross-disciplinary formalisation of biomedical investigations, but it has its limitations. OBI defines investigations and study design executions in such a way that they cannot have inputs. For example, hypotheses formed in obi:hypothesis generating investigation (an investigation in which data is generated and analysed with the purpose of generating new hypothesis) cannot be passed to obi:hypothesis driven investigation (an investigation with the goal to test one or more hypothesis) (see also the classes expo:hypothesis forming investigation and expo:hypothesis generating investigation which have been introduced before OBI [ 5 ]).

To overcome these difficulties both LABORS and DDI in addition to the class obi:investigation define a number of structural research units: study, cycle of study, trial, and replicate , mainly according to the hypotheses tested within the research unit. For example, replicates test identical hypotheses, and have identical study designs; and cycles of study test hypotheses sets in cycles (for more detail and definitions of the research units see [ 12 ]).

OBI aims to represent the most typical investigations in biomedicine. Biomedical investigations are often complicated, but they are rarely as complex as the automated investigations run by robot scientists. Therefore, we do not propose that the OBI Consortium has to define or import more structural research units in order to support the representation of automated investigations - although in the near future it may become a necessity. We instead suggest that the definition for the key class obi:investigation should be changed in such a way so biomedical investigations can have research hypotheses as specified inputs.

BFO limitations

The ontological representations of biomedical research have more serious limitations than those discussed in the previous section. The concern is how suitable they are for the representation of theories, models, and research hypotheses – essential components of science [ 22 ]. Contemporary biology is complex, multidisciplinary and information-rich science. It necessarily produces diverse and often competing theories and conclusions, alternative hypotheses, data conceptualizations and interpretations. Ontologies as formal representations of knowledge should enable common understanding of key elements of biological knowledge and support knowledge sharing and exchange.

Open Biomedical Ontologies (OBO) are designed to support annotation of biological data and results, multidisciplinary cross-domain queries, management and exchange of biomedical knowledge [ 23 ]. Members of the OBO Foundry are committed to using the same designing principles in order to ensure their interoperability and orthogonality. OBO Foundry recommends using BFO as an upper ontology to ensure that OBO-ontologies are compatibly organised [ 24 ]. The advantage of such an approach is that ontologies can be developed and curated in parallel without duplication of efforts, and that OBO-ontologies can be combined if applications require it.

However, due to their adherence to BFO, the OBO-ontologies are limited to only classes with instances in the real world. BFO does not allow the inclusion of universals (entities which can be instantiated in many things) that have no instances in the reality into BFO-based ontologies, and considers them to be outside of the realistic approach [ 16 , 19 , 22 ]. Thus, from the BFO point of view, unconfirmed theories, models, hypotheses do not exist. Yet, biologists need to communicate such key components of their research, and OBO-ontologies in the present state are straggling to support this requirement. The definition of hypotheses as textual entities (like in OBI, IAO) is a clever compromise between the biologists' needs for unconfirmed entities and BFO. Instances of textual entities do exist in reality, e.g. in printed texts. However, this is only a partial solution, and one which arguably masks the central problem; and it is ill suited for applications outside of text mining. For example, the hypothesis

at the time when Adam produced it has no instances in the reality. It exists only in the robot's memory as a number of charged transistors, and has no any associated textual entities. Only when the decision to select this hypothesis for the inclusion into a hypotheses set is made, and it is recorded in the database as a logical entity and also can be communicated to other programs and possibly to humans, does it exist as a textual entity. More importantly, it is unknown if the hypothesis statement is true or not. In fact, further experiments have confirmed that the statement is true with a certain degree of confidence. However, at a time of its confirmation the statement is no longer considered as a hypothesis, but as a result or a confirmed fact.

In general, in science there is no absolute confirmation: each hypothesis or theory with significant generality of claims is supported by evidences with a certain degree of confidence, and never reaching absolute certainty (while approaching it in some instances).

Another problem is how to include into a scientific ontology alternative and even contrary hypotheses and keep the ontology logically consistent. Researchers need a way to formalise various, sometimes contradictory, scientific discourse elements, e.g. different views, opinions, believes, and be able to reason over them. To support such needs, OBO Foundry might consider adopting a wider view on what exists.

Towards a hypothesis ontology: probabilistic reasoning

In their eloquent book, Howson and Urbach [ 25 ] argue that Bayesian inference provides the only logically consistent way of reasoning about scientific hypotheses. Competing hypotheses should be compared with each other in terms of their posterior probability on given evidence (data). When a hypothesis is formulated with the aid of probability calculus as a generative model (that is, it describes how evidence is generated stochastically according to the hypothesis), we can explicitly compute the probability of evidence. This probability is commonly computed when research requires estimating model parameters given particular scientific hypothesis. However, scientists implicitly use different prior probabilities for competing hypotheses. Any competitive scientific hypothesis must provide a positive probability of generating the already existing evidence (or it should be rejected). When the amount of evidence is moderate, prior probability of hypothesis can affect results in a profound way. Therefore, we suggest that ontological descriptions of hypotheses should explicitly address probabilistic relations between hypotheses and evidence, and the multiplicity of prior distributions over hypotheses.

Specifically, we should be able to represent prior probabilities associated with competing hypotheses. Obviously, for alternative or disjoint hypotheses (competing to explain the same evidence), these probabilities should not exceed 1 when summed. We would need to represent multiple sets of prior probabilities (associated with different experts) for the same set of hypotheses. We should be able to specify support of a given hypothesis with regard to specific evidence as a posterior probability of a research hypothesis given the dataset. The hypothesis ontology should also allow the description of relations among hypotheses (e.g., are two hypotheses compatible or mutually exclusive?).

Clearly, different scientists within the same community can weight the same set of hypotheses in very different ways. Humans are notoriously bad at estimating the uncertainty of probabilities. Therefore, we suggest that ontological descriptions of hypotheses should explicitly record how prior probabilities have been obtained and what their uncertainties are.

Finally, we should be able to represent expert–hypothesis–evidence relations (expert-hypothesis-dataset–specific posterior probabilities). We believe that ontological modelling of this type is essential for large-scale automation of scientific reasoning.

Here we summarise what lessons were learned from the representation of the automatically generated hypotheses by robots and how this might be useful for the improvement of the formulating and recording of research hypotheses produced by humans.

Explicitness. Research hypotheses should be expressed and recorded explicitly, unambiguously, and completely, so the semantic meaning of the hypothesis statement can be captured without additional information. (It is still common in the reporting of science for research hypotheses stated in the introduction to be implicitly replaced by other hypotheses in the conclusions [ 5 ]). It is also important to explicitly record hypotheses formed during investigations so that other researchers can easily find them (e.g. using text mining) and test them. This would speed up scientific progress.

Operational approach. Researchers should aim to formulate hypotheses in operational ways, so it is clear from hypotheses statements how to design experiments to test them. Hypothesis statements should contain only well defined entities and relations between them.

Systematic approach. The automated approach for hypotheses generation has an advantage of being systematic. All possible hypotheses for a study domain are considered, and the best are selected for testing. The concept of “the best hypothesis” is explicitly defined, i.e. as the most probable, cheapest, most informative one. Such a systematic approach should be adopted by humans for the assessment of research hypotheses.

Statistical significance and reliability . Researchers often report results that have been obtained without a sufficient number of experimental replicates, and therefore with unknown reliability. Adam executes 24 replicates of each study. This allows Adam to detect statistically significant differences in the yeast growth that are often missed by human-investigators [ 11 ]. This demonstrates the importance of the collecting the experimental data over a large enough number of repeated experiments to ensure statistical significance and reliable reproducibility of the results.

Learning from negative results. The hypotheses that have been rejected provide information about the domain of study. Therefore it is important to record and store the rejected hypotheses. Unfortunately, it is not a normal scientific practice to report negative results.

Authors' contributions

RDK has conceived and implemented the idea of automated hypotheses generation by robot scientists. LNS has suggested the hierarchical representation of hypotheses and hypotheses sets. LNS drafted the manuscript. AR has analysed the representation of alternative and contrarian hypotheses and theories, and drafted the discussion section. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Acknowledgements

We thank RC UK, BBSRC, SRIF 2,3 for funding the work reported in this paper. We thank all the members of the Computational Biology group at Aberystwyth University, UK for the dedicated work on the project. We thank members of the OBI Consortium for the fruitful discussions.

This article has been published as part of Journal of Biomedical Semantics Volume 2 Supplement 2, 2011: Proceedings of the Bio-Ontologies Special Interest Group Meeting 2010. The full contents of the supplement are available online at http://www.jbiomedsem.com/supplements/2/S2 .

• Gao Y, Kinoshita J, Wu E. et al. SWAN: A Distributed Knowledge Infrastructure for Alzheimer Disease Research. J. of Web Semantics. 2006; 4 (3):222–228. [ Google Scholar ]
• Whelan KE, King RD. Using a logical model to predict the growth of yeast. BMC Bioinformatics. 2008; 9 :97. doi: 10.1186/1471-2105-9-97. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• The OBI Consortium. http://obi-ontology.org
• The ART project. http://www.jisc.ac.uk/whatwedo/programmes/reppres/tools/art.aspx
• Soldatova LN, King RD. An Ontology of Scientific Experiments. J R Soc Interface. 2006; 3 (11):795–803. doi: 10.1098/rsif.2006.0134. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• The ART Corpus. http://www.ukoln.ac.uk/projects/ART_Corpus/
• Racunas SA, Shah NH, Albert I, Fedoroff NV. Hybrow: a prototype system for computer-aided hypothesis evaluation. Bioinformatics. 2004; 20 (Suppl. 1) [ PubMed ] [ Google Scholar ]
• The HyBrow project. http://www.hybrow.org
• The Yeast Proteome. http://www.proteome.com
• Large-Scale Discovery of Scientific Hypotheses project. http://arrafunding.uchicago.edu/investigators/rzhetsky_a.shtml
• King RD, Rowland J, Oliver SG. et al. The Automation of Science. Science. 2009; 324 :85–89. doi: 10.1126/science.1165620. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Qi D, King RD, Hopkins A, Bickerton R, Soldatova LN. An Ontology for Description of Drug Design Investigations. J Integr Bioinform. 2010; 7 (3) [ PubMed ] [ Google Scholar ]
• The Robot Scientist project. http://www.aber.ac.uk/en/cs/research/cb/projects/robotscientist/results/
• King RD, Whelan KE, Jones FM. et al. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature. 2004; 427 :247–252. doi: 10.1038/nature02236. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Soldatova LN, Clare A, Sparkes A, King RD. An ontology for a Robot Scientist. Bioinformatics. 2006; 22 (14):e464–e471. doi: 10.1093/bioinformatics/btl207. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith B, Ceusters W, Klagges B, Kohler J, Kumar A, Lomax J, Mungall CJ, Neuhaus F, Rector A, Rosse C. Relations in Biomedical Ontologies. Genome Biology. 2005; 6 :R46. doi: 10.1186/gb-2005-6-5-r46. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schmidt M, Lipson H. Distilling Free-Form Natural Laws from Experimental Data. Science. 2009. pp. 81–85. [ PubMed ] [ CrossRef ]
• Kuhn Th.S. The structure of scientific revolutions. University of Chicago Press; 1962. [ Google Scholar ]
• Smith B. Beyond concepts: Ontology as reality representation. Proc. FOIS. 2004.
• Ray O, Clare A, Liakata M, Soldatova L, Whelan K, King R. Towards the Automation of Scientific Method. Proc. IJCAI'09 Workshop on Abductive & Inductive Knowledge Development. 2009.
• The OBI Consortium. Modeling experimental processes with OBI. J Biomed Semantics. 2010; 1 Suppl 1 :S7. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dumontier M, Hoehndorf R. Realism for scientific ontologies. Proc. FOIS. 2010. pp. 387–399.
• The OBO Foundry. http://www.obofoundry.org/
• Smith. et al. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology. 2007; 25 :1251–1255. doi: 10.1038/nbt1346. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Howson C, Urbach P. Scientific reasoning: The Bayesian approach. Chicago: Open Court; 1993. [ Google Scholar ]
• Feynman R. "Surely You're Joking, Mr. Feynman!": Adventures of a Curious Character. W.W. Norton; 1985. [ Google Scholar ]

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.

Need a helping hand?

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.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

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

Trackbacks/Pingbacks

• What Is Research Methodology? Simple Definition (With Examples) - Grad Coach - […] Contrasted to this, a quantitative methodology is typically used when the research aims and objectives are confirmatory in nature. For example,…

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Published: 03 June 2024

A self-reinforcing cycle hypothesis in heart failure pathogenesis

• Carlos Fernandez-Patron   ORCID: orcid.org/0000-0002-8033-0645 1 ,
• Gary D. Lopaschuk   ORCID: orcid.org/0000-0003-1010-0454 2 &
• Eugenio Hardy   ORCID: orcid.org/0000-0002-8351-4255 3  

Nature Cardiovascular Research ( 2024 ) Cite this article

132 Accesses

9 Altmetric

Metrics details

• Energy metabolism
• Heart failure
• Metabolic diseases
• Multienzyme complexes

Heart failure is a progressive syndrome with high morbidity and mortality rates. Here, we suggest that chronic exposure of the heart to risk factors for heart failure damages heart mitochondria, thereby impairing energy production to levels that can suppress the heart’s ability to pump blood and repair mitochondria (both energy-consuming processes). As damaged mitochondria accumulate, the heart becomes deprived of energy in a ‘self-reinforcing cycle’, which can persist after the heart is no longer chronically exposed to (or after antagonism of) the risk factors that initiated the cycle. Together with other previously described pathological mechanisms, this proposed cycle can help explain (1) why heart failure progresses, (2) why it can recur after cessation of treatment, and (3) why heart failure is often accompanied by dysfunction of multiple organs. Ideally, therapy of heart failure syndrome would be best attempted before the self-reinforcing cycle is triggered or designed to break the self-reinforcing cycle.

This is a preview of subscription content, access via your institution

Access options

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Temporal dynamics of the multi-omic response to endurance exercise training

Loss of TRIM29 mitigates viral myocarditis by attenuating PERK-driven ER stress response in male mice

Fasting-mimicking diet causes hepatic and blood markers changes indicating reduced biological age and disease risk

Savarese, G. et al. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc. Res. 118 , 3272–3287 (2023).

Article   PubMed   Google Scholar  

Neubauer, S. The failing heart—an engine out of fuel. N. Engl. J. Med. 356 , 1140–1151 (2007). Critical appraisal of the cardiac inefficiency model of heart failure with a focus on the interactions between the cardiac mitochondrial OXPHOS pathway and the phosphagen system involving creatine .

Lopaschuk, G. D., Karwi, Q. G., Tian, R., Wende, A. R. & Abel, E. D. Cardiac energy metabolism in heart failure. Circ. Res. 128 , 1487–1513 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Packer, M., Lam, C. S. P., Lund, L. H., Maurer, M. S. & Borlaug, B. A. Characterization of the inflammatory–metabolic phenotype of heart failure with a preserved ejection fraction: a hypothesis to explain influence of sex on the evolution and potential treatment of the disease. Eur. J. Heart Fail. 22 , 1551–1567 (2020).

Decherd, G. & Visscher, M. B. Energy metabolism of the failing heart. J. Exp. Med. 59 , 195–199 (1934). A cardiac inefficiency model is first proposed to explain heart failure .

Packer, M. How should physicians view heart failure? The philosophical and physiological evolution of three conceptual models of the disease. Am. J. Cardiol. 71 , 3C–11C (1993). Three distinct conceptual models of heart failure developed over the span of 50 years have provided a rational basis for the treatment of the disease .

Article   CAS   PubMed   Google Scholar  

Seta, Y., Shan, K., Bozkurt, B., Oral, H. & Mann, D. L. Basic mechanisms in heart failure: the cytokine hypothesis. J. Card. Fail. 2 , 243–249 (1996). Cytokines, much like the neurohormones, may contribute to the development and progression of heart failure .

Brown, D. A. et al. Expert consensus document: mitochondrial function as a therapeutic target in heart failure. Nat. Rev. Cardiol. 14 , 238–250 (2017).

Packer, M. et al. Blinded withdrawal of long-term randomized treatment with empagliflozin or placebo in patients with heart failure. Circulation 148 , 1011–1022 (2023). Effects of empagliflozin treatment in patients with heart failure dissipate rapidly after withdrawal of the drug .

Mann, D. L., Barger, P. M. & Burkhoff, D. Myocardial recovery and the failing heart: myth, magic, or molecular target? J. Am. Coll. Cardiol. 60 , 2465–2472 (2012).

Article   PubMed   PubMed Central   Google Scholar  

Zhou, B. & Tian, R. Mitochondrial dysfunction in pathophysiology of heart failure. J. Clin. Invest. 128 , 3716–3726 (2018). Mitochondria-mediated mechanisms may drive the demise of the heart before the ultimate ATP depletion .

Allard, M. F., Schonekess, B. O., Henning, S. L., English, D. R. & Lopaschuk, G. D. Contribution of oxidative metabolism and glycolysis to ATP production in hypertrophied hearts. Am. J. Physiol. 267 , H742–H750 (1994).

CAS   PubMed   Google Scholar  

Jaswal, J. S., Keung, W., Wang, W., Ussher, J. R. & Lopaschuk, G. D. Targeting fatty acid and carbohydrate oxidation—a novel therapeutic intervention in the ischemic and failing heart. Biochim. Biophys. Acta 1813 , 1333–1350 (2011).

Luptak, I. et al. Long-term effects of increased glucose entry on mouse hearts during normal aging and ischemic stress. Circulation 116 , 901–909 (2007).

Aubert, G., Vega, R. B. & Kelly, D. P. Perturbations in the gene regulatory pathways controlling mitochondrial energy production in the failing heart. Biochim. Biophys. Acta 1833 , 840–847 (2013).

Fillmore, N., Wagg, C. S., Zhang, L., Fukushima, A. & Lopaschuk, G. D. Cardiac branched-chain amino acid oxidation is reduced during insulin resistance in the heart. Am. J. Physiol. Endocrinol. Metab. 315 , E1046–E1052 (2018).

Letts, J. A. & Sazanov, L. A. Clarifying the supercomplex: the higher-order organization of the mitochondrial electron transport chain. Nat. Struct. Mol. Biol. 24 , 800–808 (2017).

Mitchell, P. Coupling of phosphorylation to electron and hydrogen transfer by a chemi-osmotic type of mechanism. Nature 191 , 144–148 (1961).

Ciccarelli, M. et al. Reciprocal organ interactions during heart failure: a position paper from the ESC Working Group on Myocardial Function. Cardiovasc. Res. 117 , 2416–2433 (2021).

Bang, C. et al. Intercellular communication lessons in heart failure. Eur. J. Heart Fail. 17 , 1091–1103 (2015).

Ingwall, J. S. & Weiss, R. G. Is the failing heart energy starved? On using chemical energy to support cardiac function. Circ. Res. 95 , 135–145 (2004).

Ingwall, J. S. Energy metabolism in heart failure and remodelling. Cardiovasc. Res. 81 , 412–419 (2009). Critical appraisal of the cardiac inefficiency model .

Luptak, I. et al. Decreased ATP production and myocardial contractile reserve in metabolic heart disease. J. Mol. Cell. Cardiol. 116 , 106–114 (2018).

Abo Alrob, O. & Lopaschuk, G. D. Role of CoA and acetyl-CoA in regulating cardiac fatty acid and glucose oxidation. Biochem. Soc. Trans. 42 , 1043–1051 (2014).

Bertero, E. & Maack, C. Calcium signaling and reactive oxygen species in mitochondria. Circ. Res. 122 , 1460–1478 (2018). Alterations of intermediate substrate metabolism and oxidative stress, rather than an ATP deficit per se, are proposed to account for maladaptive remodeling and dysfunction during resting conditions .

Lopaschuk, G. D., Ussher, J. R., Folmes, C. D., Jaswal, J. S. & Stanley, W. C. Myocardial fatty acid metabolism in health and disease. Physiol. Rev. 90 , 207–258 (2010).

Bertero, E. & Maack, C. Metabolic remodelling in heart failure. Nat. Rev. Cardiol. 15 , 457–470 (2018).

Miller, B. F. & Hamilton, K. L. A perspective on the determination of mitochondrial biogenesis. Am. J. Physiol. Endocrinol. Metab. 302 , E496–E499 (2012).

Signes, A. & Fernandez-Vizarra, E. Assembly of mammalian oxidative phosphorylation complexes I–V and supercomplexes. Essays Biochem. 62 , 255–270 (2018).

Levine, B., Kalman, J., Mayer, L., Fillit, H. M. & Packer, M. Elevated circulating levels of tumour necrosis factor in severe chronic heart failure. N. Engl. J. Med. 323 , 236–241 (1990).

Packer, M. Heart failure: the most important, preventable, and treatable cardiovascular complication of type 2 diabetes. Diabetes Care 41 , 11–13 (2018).

De Jong, K. A. & Lopaschuk, G. D. Complex energy metabolic changes in heart failure with preserved ejection fraction and heart failure with reduced ejection fraction. Can. J. Cardiol. 33 , 860–871 (2017).

Murashige, D. et al. Extra-cardiac BCAA catabolism lowers blood pressure and protects from heart failure. Cell Metab. 34 , 1749–1764 (2022).

Liu, S. et al. OXPHOS deficiency activates global adaptation pathways to maintain mitochondrial membrane potential. EMBO Rep. 22 , e51606 (2021).

Lopaschuk, G. D., Rebeyka, I. M. & Allard, M. F. Metabolic modulation: a means to mend a broken heart. Circulation 105 , 140–142 (2002).

Kumar, A. A., Kelly, D. P. & Chirinos, J. A. Mitochondrial dysfunction in heart failure with preserved ejection fraction. Circulation 139 , 1435–1450 (2019).

Ridker, P. M. & Rane, M. Interleukin-6 signaling and anti-interleukin-6 therapeutics in cardiovascular disease. Circ. Res. 128 , 1728–1746 (2021).

Packer, M. et al. Empagliflozin in patients with heart failure, reduced ejection fraction, and volume overload: EMPEROR-Reduced Trial. J. Am. Coll. Cardiol. 77 , 1381–1392 (2021).

Hartupee, J. & Mann, D. L. Neurohormonal activation in heart failure with reduced ejection fraction. Nat. Rev. Cardiol. 14 , 30–38 (2017).

Shah, A. M. Parsing the role of NADPH oxidase enzymes and reactive oxygen species in heart failure. Circulation 131 , 602–604 (2015).

Teuber, J. P., Essandoh, K., Hummel, S. L., Madamanchi, N. R. & Brody, M. J. NADPH oxidases in diastolic dysfunction and heart failure with preserved ejection fraction. Antioxidants 11 , 1822 (2022).

Wang, X., Bosonea, A. M. & Fernandez-Patron, C. Metalloproteinases: key and common mediators of multiple GPCRs and candidate therapeutic targets in models of hypertensive cardiac disease. Drug Discov. Today Dis. Models 9 , e103–e108 (2012).

Hao, L., Nishimura, T., Wo, H. & Fernandez-Patron, C. Vascular responses to α1-adrenergic receptors in small rat mesenteric arteries depend on mitochondrial reactive oxygen species. Arterioscler. Thromb. Vasc. Biol. 26 , 819–825 (2006).

Gallo, L. A., Wright, E. M. & Vallon, V. Probing SGLT2 as a therapeutic target for diabetes: basic physiology and consequences. Diab. Vasc. Dis. Res. 12 , 78–89 (2015).

Packer, M. et al. Effect of empagliflozin on worsening heart failure events in patients with heart failure and preserved ejection fraction: EMPEROR-Preserved Trial. Circulation 144 , 1284–1294 (2021).

Lopaschuk, G. D. & Verma, S. Mechanisms of cardiovascular benefits of sodium glucose co-transporter 2 (SGLT2) inhibitors: a state-of-the-art review. JACC Basic Transl. Sci. 5 , 632–644 (2020).

Mazidi, M., Rezaie, P., Gao, H. K. & Kengne, A. P. Effect of sodium–glucose cotransport-2 inhibitors on blood pressure in people with type 2 diabetes mellitus: a systematic review and meta-analysis of 43 randomized control trials with 22 528 patients. J. Am. Heart Assoc. 6 , e004007 (2017).

Packer, M. Evolution of the neurohormonal hypothesis to explain the progression of chronic heart failure. Eur. Heart J. 16 , 4–6 (1995).

Byrne, N. J. et al. Empagliflozin blunts worsening cardiac dysfunction associated with reduced NLRP3 (nucleotide-binding domain-like receptor protein 3) inflammasome activation in heart failure. Circ. Heart. Fail. 13 , e006277 (2020).

Pereira, M. J. & Eriksson, J. W. Emerging role of SGLT-2 inhibitors for the treatment of obesity. Drugs 79 , 219–230 (2019).

Packer, M., Anker, S. D., Butler, J., Filippatos, G. & Zannad, F. Effects of sodium–glucose cotransporter 2 inhibitors for the treatment of patients with heart failure: proposal of a novel mechanism of action. JAMA Cardiol. 2 , 1025–1029 (2017).

Baartscheer, A. et al. Empagliflozin decreases myocardial cytoplasmic Na + through inhibition of the cardiac Na + /H + exchanger in rats and rabbits. Diabetologia 60 , 568–573 (2017).

Mentzer, R. M. Jr. et al. Sodium–hydrogen exchange inhibition by cariporide to reduce the risk of ischemic cardiac events in patients undergoing coronary artery bypass grafting: results of the EXPEDITION study. Ann. Thorac. Surg. 85 , 1261–1270 (2008).

Drakos, S. G. et al. Distinct transcriptomic and proteomic profile specifies patients who have heart failure with potential of myocardial recovery on mechanical unloading and circulatory support. Circulation 147 , 409–424 (2023).

Lopez-Campistrous, A. et al. Mitochondrial dysfunction in the hypertensive rat brain: respiratory complexes exhibit assembly defects in hypertension. Hypertension 51 , 412–419 (2008).

Ghezzi, D. & Zeviani, M. Human diseases associated with defects in assembly of OXPHOS complexes. Essays Biochem. 62 , 271–286 (2018).

Ait-Aissa, K. et al. Mitochondrial oxidative phosphorylation defect in the heart of subjects with coronary artery disease. Sci. Rep. 9 , 7623 (2019).

Gureev, A. P., Shaforostova, E. A. & Popov, V. N. Regulation of mitochondrial biogenesis as a way for active longevity: interaction between the Nrf2 and PGC-1α signaling pathways. Front. Genet. 10 , 435 (2019).

Teintze, M., Slaughter, M., Weiss, H. & Neupert, W. Biogenesis of mitochondrial ubiquinol:cytochrome c reductase (cytochrome bc 1 complex). Precursor proteins and their transfer into mitochondria. J. Biol. Chem. 257 , 10364–10371 (1982).

Tang, J. X., Thompson, K., Taylor, R. W. & Olahova, M. Mitochondrial OXPHOS biogenesis: co-regulation of protein synthesis, import, and assembly pathways. Int. J. Mol. Sci. 21 , 3820 (2020).

Marchingo, J. M. & Cantrell, D. A. Protein synthesis, degradation, and energy metabolism in T cell immunity. Cell. Mol. Immunol. 19 , 303–315 (2022).

Dyck, J. R. & Lopaschuk, G. D. AMPK alterations in cardiac physiology and pathology: enemy or ally? J. Physiol. 574 , 95–112 (2006).

Litvinukova, M. et al. Cells of the adult human heart. Nature 588 , 466–472 (2020).

Mann, D. L. Innate immunity and the failing heart: the cytokine hypothesis revisited. Circ. Res. 116 , 1254–1268 (2015).

Diebold, L. P. et al. Mitochondrial complex III is necessary for endothelial cell proliferation during angiogenesis. Nat. Metab. 1 , 158–171 (2019).

Hargreaves, M. & Spriet, L. L. Skeletal muscle energy metabolism during exercise. Nat. Metab. 2 , 817–828 (2020).

Shulman, R. G. & Rothman, D. L. The ‘glycogen shunt’ in exercising muscle: a role for glycogen in muscle energetics and fatigue. Proc. Natl Acad. Sci. USA 98 , 457–461 (2001).

Pandey, A. et al. Exercise intolerance in older adults with heart failure with preserved ejection fraction: JACC State-of-the-Art Review. J. Am. Coll. Cardiol. 78 , 1166–1187 (2021).

Nilsson, K. R., Duscha, B. D., Hranitzky, P. M. & Kraus, W. E. Chronic heart failure and exercise intolerance: the hemodynamic paradox. Curr. Cardiol. Rev. 4 , 92–100 (2008).

Lawson, C. A. et al. Outcome trends in people with heart failure, type 2 diabetes mellitus and chronic kidney disease in the UK over twenty years. EClinicalMedicine 32 , 100739 (2021).

Hajduk, A. M., Kiefe, C. I., Person, S. D., Gore, J. G. & Saczynski, J. S. Cognitive change in heart failure: a systematic review. Circ. Cardiovasc. Qual. Outcomes 6 , 451–460 (2013).

Packer, M. Does a target dose or a target heart rate matter to outcomes when prescribing β-blockers to patients with chronic heart failure? Circ. Cardiovasc. Qual. Outcomes 11 , e004605 (2018).

Ide, T. et al. Mitochondrial electron transport complex I is a potential source of oxygen free radicals in the failing myocardium. Circ. Res. 85 , 357–363 (1999).

Hansson, A. et al. A switch in metabolism precedes increased mitochondrial biogenesis in respiratory chain-deficient mouse hearts. Proc. Natl Acad. Sci. USA 101 , 3136–3141 (2004).

Bugger, H. et al. Proteomic remodelling of mitochondrial oxidative pathways in pressure overload-induced heart failure. Cardiovasc. Res. 85 , 376–384 (2010).

Wang, X. et al. Cardiac disruption of SDHAF4-mediated mitochondrial complex II assembly promotes dilated cardiomyopathy. Nat. Commun. 13 , 3947 (2022).

Sihag, S., Cresci, S., Li, A. Y., Sucharov, C. C. & Lehman, J. J. PGC-1α and ERRα target gene downregulation is a signature of the failing human heart. J. Mol. Cell. Cardiol. 46 , 201–212 (2009).

Karamanlidis, G. et al. Mitochondrial complex I deficiency increases protein acetylation and accelerates heart failure. Cell Metab. 18 , 239–250 (2013).

Ke, B. X. et al. Tissue-specific splicing of an Ndufs6 gene-trap insertion generates a mitochondrial complex I deficiency-specific cardiomyopathy. Proc. Natl Acad. Sci. USA 109 , 6165–6170 (2012).

Arany, Z. et al. Transcriptional coactivator PGC-1α controls the energy state and contractile function of cardiac muscle. Cell Metab. 1 , 259–271 (2005).

Liu, T. et al. Mitochondrial proteome remodelling in ischemic heart failure. Life Sci. 101 , 27–36 (2014).

Hou, T. et al. NDUFAB1 confers cardio-protection by enhancing mitochondrial bioenergetics through coordination of respiratory complex and supercomplex assembly. Cell Res. 29 , 754–766 (2019).

Sebastiao, M. J. et al. Unveiling human proteome signatures of heart failure with preserved ejection fraction. Biomedicines 10 , 2943 (2022).

Marin-Garcia, J., Goldenthal, M. J., Pierpont, M. E. & Ananthakrishnan, R. Impaired mitochondrial function in idiopathic dilated cardiomyopathy: biochemical and molecular analysis. J. Card. Fail. 1 , 285–291 (1995).

Unverferth, D. V., Lee, S. W. & Wallick, E. T. Human myocardial adenosine triphosphatase activities in health and heart failure. Am. Heart J. 115 , 139–146 (1988).

Ide, T. et al. Mitochondrial DNA damage and dysfunction associated with oxidative stress in failing hearts after myocardial infarction. Circ. Res. 88 , 529–535 (2001).

Jullig, M. et al. Is the failing heart out of fuel or a worn engine running rich? A study of mitochondria in old spontaneously hypertensive rats. Proteomics 8 , 2556–2572 (2008).

Quigley, A. F., Kapsa, R. M., Esmore, D., Hale, G. & Byrne, E. Mitochondrial respiratory chain activity in idiopathic dilated cardiomyopathy. J. Card. Fail. 6 , 47–55 (2000).

Rosca, M. G. et al. Cardiac mitochondria in heart failure: decrease in respirasomes and oxidative phosphorylation. Cardiovasc. Res. 80 , 30–39 (2008).

Gomez, L. A., Monette, J. S., Chavez, J. D., Maier, C. S. & Hagen, T. M. Supercomplexes of the mitochondrial electron transport chain decline in the aging rat heart. Arch. Biochem. Biophys. 490 , 30–35 (2009).

Goldberg, I. J., Trent, C. M. & Schulze, P. C. Lipid metabolism and toxicity in the heart. Cell Metab. 15 , 805–812 (2012).

Mookerjee, S. A., Gerencser, A. A., Nicholls, D. G. & Brand, M. D. Quantifying intracellular rates of glycolytic and oxidative ATP production and consumption using extracellular flux measurements. J. Biol. Chem. 293 , 12649–12652 (2018).

Martinez-Reyes, I. & Chandel, N. S. Mitochondrial TCA cycle metabolites control physiology and disease. Nat. Commun. 11 , 102 (2020).

Shi, Y. et al. Epigenetic regulation in cardiovascular disease: mechanisms and advances in clinical trials. Signal Transduct. Target. Ther. 7 , 200 (2022).

Zhang, X. et al. MicroRNA-195 regulates metabolism in failing myocardium via alterations in sirtuin 3 expression and mitochondrial protein acetylation. Circulation 137 , 2052–2067 (2018).

Vinothkumar, K. R., Zhu, J. & Hirst, J. Architecture of mammalian respiratory complex I. Nature 515 , 80–84 (2014).

Du, Z. et al. Structure of the human respiratory complex II. Proc. Natl Acad. Sci. USA 120 , e2216713120 (2023).

Solmaz, S. R. & Hunte, C. Structure of complex III with bound cytochrome c in reduced state and definition of a minimal core interface for electron transfer. J. Biol. Chem. 283 , 17542–17549 (2008).

Watt, I. N., Montgomery, M. G., Runswick, M. J., Leslie, A. G. & Walker, J. E. Bioenergetic cost of making an adenosine triphosphate molecule in animal mitochondria. Proc. Natl Acad. Sci. USA 107 , 16823–16827 (2010).

He, J. et al. Assembly of the membrane domain of ATP synthase in human mitochondria. Proc. Natl Acad. Sci. USA 115 , 2988–2993 (2018).

Download references

Author information

Authors and affiliations.

Cardiovascular Research Centre, Department of Biochemistry, Faculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, Alberta, Canada

Carlos Fernandez-Patron

Cardiovascular Research Centre, Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada

Gary D. Lopaschuk

Center of Molecular Immunology, Havana, Cuba

Eugenio Hardy

You can also search for this author in PubMed   Google Scholar

Contributions

C.F.-P. conceived the hypothesis. C.F.-P. and E.H. conceived, wrote and edited the manuscript and drafted the figures. G.D.L. made key edits and additions to the intellectual content and figures. All authors contributed to the critical analysis of the literature.

Corresponding authors

Correspondence to Carlos Fernandez-Patron or Eugenio Hardy .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Cardiovascular Research thanks Edoardo Bertero and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Fernandez-Patron, C., Lopaschuk, G.D. & Hardy, E. A self-reinforcing cycle hypothesis in heart failure pathogenesis. Nat Cardiovasc Res (2024). https://doi.org/10.1038/s44161-024-00480-6

Download citation

Received : 04 January 2024

Accepted : 25 April 2024

Published : 03 June 2024

DOI : https://doi.org/10.1038/s44161-024-00480-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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!

This is awesome.Wow.

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.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

It awesome. It has really positioned me in my research project

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Reporting Research

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for data interpretation

In research, choosing the right approach to understand data is crucial for deriving meaningful insights.…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right approach

The process of choosing the right research design can put ourselves at the crossroads of…

• Industry News

COPE Forum Discussion Highlights Challenges and Urges Clarity in Institutional Authorship Standards

The COPE forum discussion held in December 2023 initiated with a fundamental question — is…

• Career Corner

Unlocking the Power of Networking in Academic Conferences

Embarking on your first academic conference experience? Fear not, we got you covered! Academic conferences…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

How to Design Effective Research Questionnaires for Robust Findings

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

As a researcher, what do you consider most when choosing an image manipulation detector?

Our universe may be connected to an anti-universe, suggests scientist

The standard model of cosmology, which explains the universe using regular matter, dark energy, and cold dark matter (CDM), has been in place for decades. However, mysteries persist, such as the direct observation of dark matter and the controversial nature of dark energy. Naman Kumar, a PhD student at the Indian Institute of Technology , Gandhinagar, has proposed a new model that eliminates dark energy from the equation. This new model suggests a connection between our universe and an anti-universe.

New model proposes anti-universe as solution

Kumar's new model, proposed in a paper published in the journal Gravitation and Cosmology , suggests that an anti-universe with a reverse time flow could be the solution. He stated, "However, there is a price to pay. We need a partner anti-universe whose time flow is oppositely related to our universe." This idea is not entirely new, as a global team of researchers previously suggested a similar concept earlier this year.

Hypothesis on universe's accelerated expansion

Kumar's proposal is a working hypothesis pertaining to the observed accelerated expansion of the universe, which he describes as "one of the greatest puzzles in our understanding of the cosmos." As our ability to observe and study the universe improves, astronomers are slowly moving closer to a potential explanation for this discrepancy. Kumar believes that an inverted world where time flows backward, could be an elegant solution to this cosmic puzzle.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

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."

for Education

• Google Classroom
• Google Workspace Admin
• Google Cloud

Gemini for Google Workspace is here. Transform education with the help of Google’s most advanced AI widely available today.

Gemini for google workspace is here., products that power education.

Google for Education tools work together to transform teaching and learning so every student and educator can pursue their personal potential.

• Get started with Google Workspace for Education
• Get started

Use Google tools together to drive educational impact

Support learning with google workspace for education’s simple, secure, and flexible tools.

Help your institution collaborate easily, streamline instruction, and keep the learning environment safer with tools designed for students and teachers.

Manage, measure, and enrich learning experiences with Google Classroom

Provide an easy way for educators to connect with their students and manage their classes with an all-in-one place for teaching and learning.

Manage your education ecosystem with Chromebook

Equip your school with versatile, intuitive, and secure devices designed for next-generation teaching and learning.

Google Workspace for Education

Unlock the full potential of Google Workspace by using tools together.

Google Docs, Sheets & Slides

Create, collaborate, and edit with others in Docs, Sheets, Forms, Slides, and more.

Google Meet

Enable teachers, students, and admins to work together from anywhere.

Admin Console

Set policies, control access, monitor data, and quickly respond to threats.

Create new assignments

Create assignments, quizzes, and lesson content for your students.

Track student grades

Monitor progress and gain visibility with a holistic view of student achievement.

Simplify classroom management

Easily switch between Stream, Classwork, People, and Grades—even when you are offline.

Devices for everyone

Choose powerfully simple devices for everyone in your school.

Automatic updates

Keep your devices secure with updates that happen in the background.

Accessibility built-in

Customize learning experiences with select-to-speak, Chromevox, magnifiers and more.

Centralized Admin Console

Monitor your institution's domain with full visibility and control.

Equip your school with tools for success

Education leaders.

DELL LATITUDE 7410 CHROMEBOOK ENTERPRISE

Bring flexible innovation to your school at scale

Spend less time on administrative tasks and more time making an impact on student education. Equip your teachers with tools, resources, and professional development so they can focus on their students.

• Discover K-12 solutions
• Connect with a partner
• Read customer stories

ASUS CHROMEBOOK FLIP CX5

Easily deploy educational tools across your institution

Get diverse tools in the hands of students and educators with minimal effort. Maintain all of your institution’s tech from one place. Monitor info-sec, control access, and quickly make changes across your network using the centralized admin console.

• Learn about privacy & security
• Search through setup guides
• Get help with products
• Set up and use premium features

HP CHROMEBOOK X360 14B

Save time and enrich student learning with easy-to-use tools

Streamline class management while providing every student with individualized learning experiences. Create, customize, and collaborate on lessons to help learners grow their skills. Use virtual classrooms to promote learning any time, anywhere. Keep meetings, documents, and reports all within reach.

• Learn how to use products
• Get professionally certified
• Connect with other teachers

Getting started is simple

Choose a product to learn more

• Try out Workspace at no cost
• Learn about all editions
• Try out Google Classroom at no cost
• Learn about Classroom upgrades
• Explore App Hub
• Learn about Chromebooks
• Find a Chromebook

Have specific requirements or interested in upgrading?

• Contact sales
• Find a partner

What’s happening in education

New chromebook plus for educators, powered by ai, four new ways we’re partnering with the disability community, how we’re building accessibility into our chromebooks around the world, more ways to connect:.

We’re here to help

Learn more about our products, find a purchase partner, and get specific answers from our support team any time.

• Get support

You're now viewing content for a different region.

For content more relevant to your region, we suggest:

Sign up here for updates, insights, resources, and more.

• Games & Quizzes
• History & Society
• Science & Tech
• Biographies
• Animals & Nature
• Geography & Travel
• Arts & Culture
• On This Day
• One Good Fact
• New Articles
• Lifestyles & Social Issues
• Philosophy & Religion
• Politics, Law & Government
• World History
• Health & Medicine
• Browse Biographies
• Birds, Reptiles & Other Vertebrates
• Bugs, Mollusks & Other Invertebrates
• Environment
• Fossils & Geologic Time
• Entertainment & Pop Culture
• Sports & Recreation
• Visual Arts
• Demystified
• Image Galleries
• Infographics
• Top Questions
• Britannica Kids
• Saving Earth
• Space Next 50
• Student Center

• When did science begin?
• Where was science invented?

scientific hypothesis

Our editors will review what you’ve submitted and determine whether to revise the article.

• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

Why the Pandemic Probably Started in a Lab, in 5 Key Points

By Alina Chan

Dr. Chan is a molecular biologist at the Broad Institute of M.I.T. and Harvard, and a co-author of “Viral: The Search for the Origin of Covid-19.”

This article has been updated to reflect news developments.

On Monday, Dr. Anthony Fauci returned to the halls of Congress and testified before the House subcommittee investigating the Covid-19 pandemic. He was questioned about several topics related to the government’s handling of Covid-19, including how the National Institute of Allergy and Infectious Diseases, which he directed until retiring in 2022, supported risky virus work at a Chinese institute whose research may have caused the pandemic.

For more than four years, reflexive partisan politics have derailed the search for the truth about a catastrophe that has touched us all. It has been estimated that at least 25 million people around the world have died because of Covid-19, with over a million of those deaths in the United States.

Although how the pandemic started has been hotly debated, a growing volume of evidence — gleaned from public records released under the Freedom of Information Act, digital sleuthing through online databases, scientific papers analyzing the virus and its spread, and leaks from within the U.S. government — suggests that the pandemic most likely occurred because a virus escaped from a research lab in Wuhan, China. If so, it would be the most costly accident in the history of science.

Here’s what we now know:

1 The SARS-like virus that caused the pandemic emerged in Wuhan, the city where the world’s foremost research lab for SARS-like viruses is located.

• At the Wuhan Institute of Virology, a team of scientists had been hunting for SARS-like viruses for over a decade, led by Shi Zhengli.
• Their research showed that the viruses most similar to SARS‑CoV‑2, the virus that caused the pandemic, circulate in bats that live r oughly 1,000 miles away from Wuhan. Scientists from Dr. Shi’s team traveled repeatedly to Yunnan province to collect these viruses and had expanded their search to Southeast Asia. Bats in other parts of China have not been found to carry viruses that are as closely related to SARS-CoV-2.

The closest known relatives to SARS-CoV-2 were found in southwestern China and in Laos.

Large cities

Mine in Yunnan province

Cave in Laos

South China Sea

The closest known relatives to SARS-CoV-2

were found in southwestern China and in Laos.

philippines

The closest known relatives to SARS-CoV-2 were found

in southwestern China and Laos.

Sources: Sarah Temmam et al., Nature; SimpleMaps

Note: Cities shown have a population of at least 200,000.

There are hundreds of large cities in China and Southeast Asia.

There are hundreds of large cities in China

and Southeast Asia.

The pandemic started roughly 1,000 miles away, in Wuhan, home to the world’s foremost SARS-like virus research lab.

The pandemic started roughly 1,000 miles away,

in Wuhan, home to the world’s foremost SARS-like virus research lab.

The pandemic started roughly 1,000 miles away, in Wuhan,

home to the world’s foremost SARS-like virus research lab.

• Even at hot spots where these viruses exist naturally near the cave bats of southwestern China and Southeast Asia, the scientists argued, as recently as 2019 , that bat coronavirus spillover into humans is rare .
• When the Covid-19 outbreak was detected, Dr. Shi initially wondered if the novel coronavirus had come from her laboratory , saying she had never expected such an outbreak to occur in Wuhan.
• The SARS‑CoV‑2 virus is exceptionally contagious and can jump from species to species like wildfire . Yet it left no known trace of infection at its source or anywhere along what would have been a thousand-mile journey before emerging in Wuhan.

2 The year before the outbreak, the Wuhan institute, working with U.S. partners, had proposed creating viruses with SARS‑CoV‑2’s defining feature.

• Dr. Shi’s group was fascinated by how coronaviruses jump from species to species. To find viruses, they took samples from bats and other animals , as well as from sick people living near animals carrying these viruses or associated with the wildlife trade. Much of this work was conducted in partnership with the EcoHealth Alliance, a U.S.-based scientific organization that, since 2002, has been awarded over $80 million in federal funding to research the risks of emerging infectious diseases.
• The laboratory pursued risky research that resulted in viruses becoming more infectious : Coronaviruses were grown from samples from infected animals and genetically reconstructed and recombined to create new viruses unknown in nature. These new viruses were passed through cells from bats, pigs, primates and humans and were used to infect civets and humanized mice (mice modified with human genes). In essence, this process forced these viruses to adapt to new host species, and the viruses with mutations that allowed them to thrive emerged as victors.
• By 2019, Dr. Shi’s group had published a database describing more than 22,000 collected wildlife samples. But external access was shut off in the fall of 2019, and the database was not shared with American collaborators even after the pandemic started , when such a rich virus collection would have been most useful in tracking the origin of SARS‑CoV‑2. It remains unclear whether the Wuhan institute possessed a precursor of the pandemic virus.
• In 2021, The Intercept published a leaked 2018 grant proposal for a research project named Defuse , which had been written as a collaboration between EcoHealth, the Wuhan institute and Ralph Baric at the University of North Carolina, who had been on the cutting edge of coronavirus research for years. The proposal described plans to create viruses strikingly similar to SARS‑CoV‑2.
• Coronaviruses bear their name because their surface is studded with protein spikes, like a spiky crown, which they use to enter animal cells. T he Defuse project proposed to search for and create SARS-like viruses carrying spikes with a unique feature: a furin cleavage site — the same feature that enhances SARS‑CoV‑2’s infectiousness in humans, making it capable of causing a pandemic. Defuse was never funded by the United States . However, in his testimony on Monday, Dr. Fauci explained that the Wuhan institute would not need to rely on U.S. funding to pursue research independently.

The Wuhan lab ran risky experiments to learn about how SARS-like viruses might infect humans.

1. Collect SARS-like viruses from bats and other wild animals, as well as from people exposed to them.

2. Identify high-risk viruses by screening for spike proteins that facilitate infection of human cells.

2. Identify high-risk viruses by screening for spike proteins that facilitate infection of

human cells.

In Defuse, the scientists proposed to add a furin cleavage site to the spike protein.

3. Create new coronaviruses by inserting spike proteins or other features that could make the viruses more infectious in humans.

4. Infect human cells, civets and humanized mice with the new coronaviruses, to determine how dangerous they might be.

• While it’s possible that the furin cleavage site could have evolved naturally (as seen in some distantly related coronaviruses), out of the hundreds of SARS-like viruses cataloged by scientists, SARS‑CoV‑2 is the only one known to possess a furin cleavage site in its spike. And the genetic data suggest that the virus had only recently gained the furin cleavage site before it started the pandemic.
• Ultimately, a never-before-seen SARS-like virus with a newly introduced furin cleavage site, matching the description in the Wuhan institute’s Defuse proposal, caused an outbreak in Wuhan less than two years after the proposal was drafted.
• When the Wuhan scientists published their seminal paper about Covid-19 as the pandemic roared to life in 2020, they did not mention the virus’s furin cleavage site — a feature they should have been on the lookout for, according to their own grant proposal, and a feature quickly recognized by other scientists.
• Worse still, as the pandemic raged, their American collaborators failed to publicly reveal the existence of the Defuse proposal. The president of EcoHealth, Peter Daszak, recently admitted to Congress that he doesn’t know about virus samples collected by the Wuhan institute after 2015 and never asked the lab’s scientists if they had started the work described in Defuse. In May, citing failures in EcoHealth’s monitoring of risky experiments conducted at the Wuhan lab, the Biden administration suspended all federal funding for the organization and Dr. Daszak, and initiated proceedings to bar them from receiving future grants. In his testimony on Monday, Dr. Fauci said that he supported the decision to suspend and bar EcoHealth.
• Separately, Dr. Baric described the competitive dynamic between his research group and the institute when he told Congress that the Wuhan scientists would probably not have shared their most interesting newly discovered viruses with him . Documents and email correspondence between the institute and Dr. Baric are still being withheld from the public while their release is fiercely contested in litigation.
• In the end, American partners very likely knew of only a fraction of the research done in Wuhan. According to U.S. intelligence sources, some of the institute’s virus research was classified or conducted with or on behalf of the Chinese military . In the congressional hearing on Monday, Dr. Fauci repeatedly acknowledged the lack of visibility into experiments conducted at the Wuhan institute, saying, “None of us can know everything that’s going on in China, or in Wuhan, or what have you. And that’s the reason why — I say today, and I’ve said at the T.I.,” referring to his transcribed interview with the subcommittee, “I keep an open mind as to what the origin is.”

3 The Wuhan lab pursued this type of work under low biosafety conditions that could not have contained an airborne virus as infectious as SARS‑CoV‑2.

• Labs working with live viruses generally operate at one of four biosafety levels (known in ascending order of stringency as BSL-1, 2, 3 and 4) that describe the work practices that are considered sufficiently safe depending on the characteristics of each pathogen. The Wuhan institute’s scientists worked with SARS-like viruses under inappropriately low biosafety conditions .

In the United States, virologists generally use stricter Biosafety Level 3 protocols when working with SARS-like viruses.

Biosafety cabinets prevent

viral particles from escaping.

Viral particles

Personal respirators provide

a second layer of defense against breathing in the virus.

DIRECT CONTACT

Gloves prevent skin contact.

Disposable wraparound

gowns cover much of the rest of the body.

Personal respirators provide a second layer of defense against breathing in the virus.

Disposable wraparound gowns

cover much of the rest of the body.

Note: ​​Biosafety levels are not internationally standardized, and some countries use more permissive protocols than others.

The Wuhan lab had been regularly working with SARS-like viruses under Biosafety Level 2 conditions, which could not prevent a highly infectious virus like SARS-CoV-2 from escaping.

Some work is done in the open air, and masks are not required.

Less protective equipment provides more opportunities

for contamination.

Some work is done in the open air,

and masks are not required.

Less protective equipment provides more opportunities for contamination.

• In one experiment, Dr. Shi’s group genetically engineered an unexpectedly deadly SARS-like virus (not closely related to SARS‑CoV‑2) that exhibited a 10,000-fold increase in the quantity of virus in the lungs and brains of humanized mice . Wuhan institute scientists handled these live viruses at low biosafet y levels , including BSL-2.
• Even the much more stringent containment at BSL-3 cannot fully prevent SARS‑CoV‑2 from escaping . Two years into the pandemic, the virus infected a scientist in a BSL-3 laboratory in Taiwan, which was, at the time, a zero-Covid country. The scientist had been vaccinated and was tested only after losing the sense of smell. By then, more than 100 close contacts had been exposed. Human error is a source of exposure even at the highest biosafety levels , and the risks are much greater for scientists working with infectious pathogens at low biosafety.
• An early draft of the Defuse proposal stated that the Wuhan lab would do their virus work at BSL-2 to make it “highly cost-effective.” Dr. Baric added a note to the draft highlighting the importance of using BSL-3 to contain SARS-like viruses that could infect human cells, writing that “U.S. researchers will likely freak out.” Years later, after SARS‑CoV‑2 had killed millions, Dr. Baric wrote to Dr. Daszak : “I have no doubt that they followed state determined rules and did the work under BSL-2. Yes China has the right to set their own policy. You believe this was appropriate containment if you want but don’t expect me to believe it. Moreover, don’t insult my intelligence by trying to feed me this load of BS.”
• SARS‑CoV‑2 is a stealthy virus that transmits effectively through the air, causes a range of symptoms similar to those of other common respiratory diseases and can be spread by infected people before symptoms even appear. If the virus had escaped from a BSL-2 laboratory in 2019, the leak most likely would have gone undetected until too late.
• One alarming detail — leaked to The Wall Street Journal and confirmed by current and former U.S. government officials — is that scientists on Dr. Shi’s team fell ill with Covid-like symptoms in the fall of 2019 . One of the scientists had been named in the Defuse proposal as the person in charge of virus discovery work. The scientists denied having been sick .

4 The hypothesis that Covid-19 came from an animal at the Huanan Seafood Market in Wuhan is not supported by strong evidence.

• In December 2019, Chinese investigators assumed the outbreak had started at a centrally located market frequented by thousands of visitors daily. This bias in their search for early cases meant that cases unlinked to or located far away from the market would very likely have been missed. To make things worse, the Chinese authorities blocked the reporting of early cases not linked to the market and, claiming biosafety precautions, ordered the destruction of patient samples on January 3, 2020, making it nearly impossible to see the complete picture of the earliest Covid-19 cases. Information about dozens of early cases from November and December 2019 remains inaccessible.
• A pair of papers published in Science in 2022 made the best case for SARS‑CoV‑2 having emerged naturally from human-animal contact at the Wuhan market by focusing on a map of the early cases and asserting that the virus had jumped from animals into humans twice at the market in 2019. More recently, the two papers have been countered by other virologists and scientists who convincingly demonstrate that the available market evidence does not distinguish between a human superspreader event and a natural spillover at the market.
• Furthermore, the existing genetic and early case data show that all known Covid-19 cases probably stem from a single introduction of SARS‑CoV‑2 into people, and the outbreak at the Wuhan market probably happened after the virus had already been circulating in humans.

An analysis of SARS-CoV-2’s evolutionary tree shows how the virus evolved as it started to spread through humans.

SARS-COV-2 Viruses closest

to bat coronaviruses

more mutations

Source: Lv et al., Virus Evolution (2024) , as reproduced by Jesse Bloom

The viruses that infected people linked to the market were most likely not the earliest form of the virus that started the pandemic.

• Not a single infected animal has ever been confirmed at the market or in its supply chain. Without good evidence that the pandemic started at the Huanan Seafood Market, the fact that the virus emerged in Wuhan points squarely at its unique SARS-like virus laboratory.

5 Key evidence that would be expected if the virus had emerged from the wildlife trade is still missing.

In previous outbreaks of coronaviruses, scientists were able to demonstrate natural origin by collecting multiple pieces of evidence linking infected humans to infected animals.

Infected animals

Earliest known

cases exposed to

live animals

Antibody evidence

of animals and

animal traders having

been infected

Ancestral variants

of the virus found in

Documented trade

of host animals

between the area

where bats carry

closely related viruses

and the outbreak site

Infected animals found

Earliest known cases exposed to live animals

Antibody evidence of animals and animal

traders having been infected

Ancestral variants of the virus found in animals

Documented trade of host animals

between the area where bats carry closely

related viruses and the outbreak site

For SARS-CoV-2, these same key pieces of evidence are still missing , more than four years after the virus emerged.

For SARS-CoV-2, these same key pieces of evidence are still missing ,

more than four years after the virus emerged.

• Despite the intense search trained on the animal trade and people linked to the market, investigators have not reported finding any animals infected with SARS‑CoV‑2 that had not been infected by humans. Yet, infected animal sources and other connective pieces of evidence were found for the earlier SARS and MERS outbreaks as quickly as within a few days, despite the less advanced viral forensic technologies of two decades ago.
• Even though Wuhan is the home base of virus hunters with world-leading expertise in tracking novel SARS-like viruses, investigators have either failed to collect or report key evidence that would be expected if Covid-19 emerged from the wildlife trade . For example, investigators have not determined that the earliest known cases had exposure to intermediate host animals before falling ill. No antibody evidence shows that animal traders in Wuhan are regularly exposed to SARS-like viruses, as would be expected in such situations.
• With today’s technology, scientists can detect how respiratory viruses — including SARS, MERS and the flu — circulate in animals while making repeated attempts to jump across species . Thankfully, these variants usually fail to transmit well after crossing over to a new species and tend to die off after a small number of infections. In contrast, virologists and other scientists agree that SARS‑CoV‑2 required little to no adaptation to spread rapidly in humans and other animals . The virus appears to have succeeded in causing a pandemic upon its only detected jump into humans.

The pandemic could have been caused by any of hundreds of virus species, at any of tens of thousands of wildlife markets, in any of thousands of cities, and in any year. But it was a SARS-like coronavirus with a unique furin cleavage site that emerged in Wuhan, less than two years after scientists, sometimes working under inadequate biosafety conditions, proposed collecting and creating viruses of that same design.

While several natural spillover scenarios remain plausible, and we still don’t know enough about the full extent of virus research conducted at the Wuhan institute by Dr. Shi’s team and other researchers, a laboratory accident is the most parsimonious explanation of how the pandemic began.

Given what we now know, investigators should follow their strongest leads and subpoena all exchanges between the Wuhan scientists and their international partners, including unpublished research proposals, manuscripts, data and commercial orders. In particular, exchanges from 2018 and 2019 — the critical two years before the emergence of Covid-19 — are very likely to be illuminating (and require no cooperation from the Chinese government to acquire), yet they remain beyond the public’s view more than four years after the pandemic began.

Whether the pandemic started on a lab bench or in a market stall, it is undeniable that U.S. federal funding helped to build an unprecedented collection of SARS-like viruses at the Wuhan institute, as well as contributing to research that enhanced them . Advocates and funders of the institute’s research, including Dr. Fauci, should cooperate with the investigation to help identify and close the loopholes that allowed such dangerous work to occur. The world must not continue to bear the intolerable risks of research with the potential to cause pandemics .

A successful investigation of the pandemic’s root cause would have the power to break a decades-long scientific impasse on pathogen research safety, determining how governments will spend billions of dollars to prevent future pandemics. A credible investigation would also deter future acts of negligence and deceit by demonstrating that it is indeed possible to be held accountable for causing a viral pandemic. Last but not least, people of all nations need to see their leaders — and especially, their scientists — heading the charge to find out what caused this world-shaking event. Restoring public trust in science and government leadership requires it.

A thorough investigation by the U.S. government could unearth more evidence while spurring whistleblowers to find their courage and seek their moment of opportunity. It would also show the world that U.S. leaders and scientists are not afraid of what the truth behind the pandemic may be.

More on how the pandemic may have started

Where Did the Coronavirus Come From? What We Already Know Is Troubling.

Even if the coronavirus did not emerge from a lab, the groundwork for a potential disaster had been laid for years, and learning its lessons is essential to preventing others.

By Zeynep Tufekci

Why Does Bad Science on Covid’s Origin Get Hyped?

If the raccoon dog was a smoking gun, it fired blanks.

By David Wallace-Wells

A Plea for Making Virus Research Safer

A way forward for lab safety.

By Jesse Bloom

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

Follow the New York Times Opinion section on Facebook , Instagram , TikTok , WhatsApp , X and Threads .

Alina Chan ( @ayjchan ) is a molecular biologist at the Broad Institute of M.I.T. and Harvard, and a co-author of “ Viral : The Search for the Origin of Covid-19.” She was a member of the Pathogens Project , which the Bulletin of the Atomic Scientists organized to generate new thinking on responsible, high-risk pathogen research.

• Share full article

Advertisement

NRS 390 Midterm

Students also viewed