discuss the importance of hypothesis in research

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Your partner for better health, hypothesis in research: definition, types and importance .

April 21, 2020 Kusum Wagle Epidemiology 0

Table of Contents

What is Hypothesis?

• Hypothesis is a logical prediction of certain occurrences without the support of empirical confirmation or evidence.
• In scientific terms, it is a tentative theory or testable statement about the relationship between two or more variables i.e. independent and dependent variable.

Different Types of Hypothesis:

1. Simple Hypothesis:

• A Simple hypothesis is also known as composite hypothesis.
• In simple hypothesis all parameters of the distribution are specified.
• It predicts relationship between two variables i.e. the dependent and the independent variable

2. Complex Hypothesis:

• A Complex hypothesis examines relationship between two or more independent variables and two or more dependent variables.

3. Working or Research Hypothesis:

• A research hypothesis is a specific, clear prediction about the possible outcome of a scientific research study based on specific factors of the population.

4. Null Hypothesis:

• A null hypothesis is a general statement which states no relationship between two variables or two phenomena. It is usually denoted by H 0 .

5. Alternative Hypothesis:

• An alternative hypothesis is a statement which states some statistical significance between two phenomena. It is usually denoted by H 1 or H A .

6. Logical Hypothesis:

• A logical hypothesis is a planned explanation holding limited evidence.

7. Statistical Hypothesis:

• A statistical hypothesis, sometimes called confirmatory data analysis, is an assumption about a population parameter.

Although there are different types of hypothesis, the most commonly and used hypothesis are Null hypothesis and alternate hypothesis . So, what is the difference between null hypothesis and alternate hypothesis? Let’s have a look:

Major Differences Between Null Hypothesis and Alternative Hypothesis:

Importance of hypothesis:.

• It ensures the entire research methodologies are scientific and valid.
• It helps to assume the probability of research failure and progress.
• It helps to provide link to the underlying theory and specific research question.
• It helps in data analysis and measure the validity and reliability of the research.
• It provides a basis or evidence to prove the validity of the research.
• It helps to describe research study in concrete terms rather than theoretical terms.

Characteristics of Good Hypothesis:

• Should be simple.
• Should be specific.
• Should be stated in advance.

References and For More Information:

https://ocw.jhsph.edu/courses/StatisticalReasoning1/PDFs/2009/BiostatisticsLecture4.pdf

https://keydifferences.com/difference-between-type-i-and-type-ii-errors.html

https://www.khanacademy.org/math/ap-statistics/tests-significance-ap/error-probabilities-power/a/consequences-errors-significance

https://stattrek.com/hypothesis-test/hypothesis-testing.aspx

http://davidmlane.com/hyperstat/A2917.html

https://study.com/academy/lesson/what-is-a-hypothesis-definition-lesson-quiz.html

https://keydifferences.com/difference-between-null-and-alternative-hypothesis.html

https://blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesis-tests-why-we-need-to-use-hypothesis-tests-in-statistics

• Characteristics of Good Hypothesis
• complex hypothesis
• example of alternative hypothesis
• example of null hypothesis
• how is null hypothesis different to alternative hypothesis
• Importance of Hypothesis
• null hypothesis vs alternate hypothesis
• simple hypothesis
• Types of Hypotheses
• what is alternate hypothesis
• what is alternative hypothesis
• what is hypothesis?
• what is logical hypothesis
• what is null hypothesis
• what is research hypothesis
• what is statistical hypothesis
• why is hypothesis necessary

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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 ...

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16 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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

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

What is a Hypothesis in Research?

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

Research Question vs Hypothesis

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

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

How to Write Hypothesis in Research

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

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

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

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

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

Research Hypothesis Example

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

Here are a few generic examples to get you started.

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

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

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

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

Language Editing Plus

Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

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Understanding the importance of a research hypothesis

A research hypothesis is a specification of a testable prediction about what a researcher expects as the outcome of the study. It comprises certain aspects such as the population, variables, and the relationship between the variables. It states the specific role of the position of individual elements through empirical verification. When conducting research, there are certain assumptions that are made by the researcher. According to the available information, the goal is to present the expected outcome after testing them.

A hypothesis should be precise and accurate

A hypothesis is a clear statement of the information that the researcher intends to investigate. It is thus a clear statement that is essential before conducting research.

Based on this aspect, the features of the hypothesis are listed below:

1. Conceptual

The statement of the hypothesis is based on a certain concept i.e. it could be either related to the theory or the pre-assumption of the researcher about certain variables i.e. educated guess. This leads to linking the research questions of the study. It helps the collection of data and conducting analysis as per the stated concept.

People who shop at speciality stores tend to spend more on luxury brands as compared to those who shop at a department store.

2. Verbal statement

The research hypothesis represents a verbal statement in declarative form. The hypothesis is often stated in mathematical form. However, it brings in the possibility of representing the idea, assumption, or concept of the researcher in the form of words that could be tested.

The capability of students who are undergoing vocational training programs is not different from the students undergoing regular studies.

3. Empirical reference

By building a tentative relationship among concepts, hypothesis testing provides an empirical verification of a study. It helps validate the assumption of the researcher.

The quality of nursing education affects the quality of nursing practice skills.

4. Tentative relationship

It links the variables as per assumption and builds a tentative relationship. A hypothesis is initially unverified, therefore the relationship between variables is uncertain. Thus a predictable relationship is specified.

Sleep deprivation affects the productivity of an individual.

5. Tool of knowledge advancement

With help of a hypothesis statement, the researcher has the opportunity of verifying the available knowledge and having further enquiry about a concept. Thus, it helps the advancement of knowledge.

The effectiveness of social awareness programs influences the living standards of people.

The hypothesis statement provides the benefit of assessing the available information and making the appropriate prediction about the future. With the possibility of verifiability and identifying falsifiable information, researchers assess their assumptions and determine accurate conclusions.

People who are exposed to a high level of ultraviolet light tend to have a higher incidence of cancer.

7. Not moral

The hypothesis statement is not based on the consideration of moral values or ethics. It is as per the beliefs or assumptions of the researcher. However, testing and prediction are not entirely based on individual moral beliefs. For example, people having sample moral values would take the same strategy for business management. In this case, it is not the desired objective to study the business management strategy.

Neither too specific nor too general

A hypothesis should not be too general or too specific.

‘Actions of an individual would impact the health’ is too general, and ‘running would improve your health’ is too specific. Thus, the hypothesis for the above study is exercise does have an impact on the health of people.

Prediction of consequences

The hypothesis is the statement of the researcher’s assumption. Thus, it helps in predicting the ultimate outcome of the thesis.

Experience leads to better air traffic control management.

Even if the assumption of the researcher is proven false in testing, the result derived from the examination is valuable. With the presence of null and alternative hypotheses, each assessment of the hypothesis yields a valuable conclusion.

Separating irrelevant information from relevant information

 A hypothesis plays a significant role ineffectiveness of a study. It not only navigates the researcher but also prevents the researcher from building an inconclusive study. By guiding as light in the entire thesis, the hypothesis contributes to suggesting and testing the theories along with describing the legal or social phenomenon.

Navigate research

A hypothesis helps in identifying the areas that should be focused on for solving the research problem. It helps frame the concepts of study in a meaningful and effective manner. It also helps the researcher arrive at a conclusion for the study based on organized empirical data examination.

Prevents blind research

A hypothesis guides the researcher in the processes that need to be followed throughout the study. It prevents the researcher from collecting massive data and doing blind research which would prove irrelevant.

A platform for investigating activities

By examining conceptual and factual elements related to the problem of a thesis, the hypothesis provides a framework for drawing effective conclusions. It also helps stimulate further studies.

Describes a phenomenon

Each time a hypothesis is tested, more information about the concerned phenomenon is made available. Empirical support via hypothesis testing helps analyse aspects that were unexplored earlier.

Framing accurate research hypothesis statements

For the deduction of accurate and reliable outcomes from the analysis, belong stated things should be noted:

• Should never be formulated in the form of a question.
• Empirical testability of the hypothesis should be possible.
• A precise and specific statement of concept should be present.
• The hypothesis should not be contradictory to the identified concept and linkage between the variables.
• A clear specification of all the variables which are used for building relationships in the hypothesis should be present.
• The focus of a single hypothesis should only be on one issue. No multi-issue consideration should be taken while building the hypothesis i.e. could only be either relational or descriptive.
• The hypothesis should not be conflicting with the defined law of nature which is already specified as true.
• Effective tools and techniques need to be used for the verification of the hypothesis.
• The form of the hypothesis statement should be simple and understandable. Complex or conflicting statement reduces the applicability and reliability of the thesis results.
• The hypothesis should be amendable in the form that testing could be completed within a specified reasonable time.
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The Research Hypothesis: Role and Construction

• First Online: 01 January 2012

Cite this chapter

• Phyllis G. Supino EdD 3  

6030 Accesses

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

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

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

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

—Augustus De Morgan, 1872[ 1 ]—

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

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2.1 Why Is Research Important?

Learning objectives.

By the end of this section, you will be able to:

• Explain how scientific research addresses questions about behavior
• Discuss how scientific research guides public policy
• Appreciate how scientific research can be important in making personal decisions

Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession ( Figure 2.2 ). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This chapter explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.

Use of Research Information

Trying to determine which theories are and are not accepted by the scientific community can be difficult, especially in an area of research as broad as psychology. More than ever before, we have an incredible amount of information at our fingertips, and a simple internet search on any given research topic might result in a number of contradictory studies. In these cases, we are witnessing the scientific community going through the process of reaching a consensus, and it could be quite some time before a consensus emerges. For example, the explosion in our use of technology has led researchers to question whether this ultimately helps or hinders us. The use and implementation of technology in educational settings has become widespread over the last few decades. Researchers are coming to different conclusions regarding the use of technology. To illustrate this point, a study investigating a smartphone app targeting surgery residents (graduate students in surgery training) found that the use of this app can increase student engagement and raise test scores (Shaw & Tan, 2015). Conversely, another study found that the use of technology in undergraduate student populations had negative impacts on sleep, communication, and time management skills (Massimini & Peterson, 2009). Until sufficient amounts of research have been conducted, there will be no clear consensus on the effects that technology has on a student's acquisition of knowledge, study skills, and mental health.

In the meantime, we should strive to think critically about the information we encounter by exercising a degree of healthy skepticism. When someone makes a claim, we should examine the claim from a number of different perspectives: what is the expertise of the person making the claim, what might they gain if the claim is valid, does the claim seem justified given the evidence, and what do other researchers think of the claim? This is especially important when we consider how much information in advertising campaigns and on the internet claims to be based on “scientific evidence” when in actuality it is a belief or perspective of just a few individuals trying to sell a product or draw attention to their perspectives.

We should be informed consumers of the information made available to us because decisions based on this information have significant consequences. One such consequence can be seen in politics and public policy. Imagine that you have been elected as the governor of your state. One of your responsibilities is to manage the state budget and determine how to best spend your constituents’ tax dollars. As the new governor, you need to decide whether to continue funding early intervention programs. These programs are designed to help children who come from low-income backgrounds, have special needs, or face other disadvantages. These programs may involve providing a wide variety of services to maximize the children's development and position them for optimal levels of success in school and later in life (Blann, 2005). While such programs sound appealing, you would want to be sure that they also proved effective before investing additional money in these programs. Fortunately, psychologists and other scientists have conducted vast amounts of research on such programs and, in general, the programs are found to be effective (Neil & Christensen, 2009; Peters-Scheffer, Didden, Korzilius, & Sturmey, 2011). While not all programs are equally effective, and the short-term effects of many such programs are more pronounced, there is reason to believe that many of these programs produce long-term benefits for participants (Barnett, 2011). If you are committed to being a good steward of taxpayer money, you would want to look at research. Which programs are most effective? What characteristics of these programs make them effective? Which programs promote the best outcomes? After examining the research, you would be best equipped to make decisions about which programs to fund.

Link to Learning

Watch this video about early childhood program effectiveness to learn how scientists evaluate effectiveness and how best to invest money into programs that are most effective.

Ultimately, it is not just politicians who can benefit from using research in guiding their decisions. We all might look to research from time to time when making decisions in our lives. Imagine that your sister, Maria, expresses concern about her two-year-old child, Umberto. Umberto does not speak as much or as clearly as the other children in his daycare or others in the family. Umberto's pediatrician undertakes some screening and recommends an evaluation by a speech pathologist, but does not refer Maria to any other specialists. Maria is concerned that Umberto's speech delays are signs of a developmental disorder, but Umberto's pediatrician does not; she sees indications of differences in Umberto's jaw and facial muscles. Hearing this, you do some internet searches, but you are overwhelmed by the breadth of information and the wide array of sources. You see blog posts, top-ten lists, advertisements from healthcare providers, and recommendations from several advocacy organizations. Why are there so many sites? Which are based in research, and which are not?

In the end, research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.

NOTABLE RESEARCHERS

Psychological research has a long history involving important figures from diverse backgrounds. While the introductory chapter discussed several researchers who made significant contributions to the discipline, there are many more individuals who deserve attention in considering how psychology has advanced as a science through their work ( Figure 2.3 ). For instance, Margaret Floy Washburn (1871–1939) was the first woman to earn a PhD in psychology. Her research focused on animal behavior and cognition (Margaret Floy Washburn, PhD, n.d.). Mary Whiton Calkins (1863–1930) was a preeminent first-generation American psychologist who opposed the behaviorist movement, conducted significant research into memory, and established one of the earliest experimental psychology labs in the United States (Mary Whiton Calkins, n.d.).

Francis Sumner (1895–1954) was the first African American to receive a PhD in psychology in 1920. His dissertation focused on issues related to psychoanalysis. Sumner also had research interests in racial bias and educational justice. Sumner was one of the founders of Howard University’s department of psychology, and because of his accomplishments, he is sometimes referred to as the “Father of Black Psychology.” Thirteen years later, Inez Beverly Prosser (1895–1934) became the first African American woman to receive a PhD in psychology. Prosser’s research highlighted issues related to education in segregated versus integrated schools, and ultimately, her work was very influential in the hallmark Brown v. Board of Education Supreme Court ruling that segregation of public schools was unconstitutional (Ethnicity and Health in America Series: Featured Psychologists, n.d.).

Although the establishment of psychology’s scientific roots occurred first in Europe and the United States, it did not take much time until researchers from around the world began to establish their own laboratories and research programs. For example, some of the first experimental psychology laboratories in South America were founded by Horatio Piñero (1869–1919) at two institutions in Buenos Aires, Argentina (Godoy & Brussino, 2010). In India, Gunamudian David Boaz (1908–1965) and Narendra Nath Sen Gupta (1889–1944) established the first independent departments of psychology at the University of Madras and the University of Calcutta, respectively. These developments provided an opportunity for Indian researchers to make important contributions to the field (Gunamudian David Boaz, n.d.; Narendra Nath Sen Gupta, n.d.).

When the American Psychological Association (APA) was first founded in 1892, all of the members were White males (Women and Minorities in Psychology, n.d.). However, by 1905, Mary Whiton Calkins was elected as the first female president of the APA, and by 1946, nearly one-quarter of American psychologists were female. Psychology became a popular degree option for students enrolled in the nation’s historically Black higher education institutions, increasing the number of Black Americans who went on to become psychologists. Given demographic shifts occurring in the United States and increased access to higher educational opportunities among historically underrepresented populations, there is reason to hope that the diversity of the field will increasingly match the larger population, and that the research contributions made by the psychologists of the future will better serve people of all backgrounds (Women and Minorities in Psychology, n.d.).

The Process of Scientific Research

Scientific knowledge is advanced through a process known as the scientific method . Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. The types of reasoning within the circle are called deductive and inductive. In deductive reasoning , ideas are tested in the real world; in inductive reasoning , real-world observations lead to new ideas ( Figure 2.4 ). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.

In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world. If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider this argument: all ducks are born with the ability to see. Quackers is a duck. Therefore, Quackers was born with the ability to see. Scientists use deductive reasoning to empirically test their hypotheses. Returning to the example of the ducks, researchers might design a study to test the hypothesis that if all living things require energy to survive, then ducks will be found to require energy to survive.

Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For instance, you may notice that your favorite fruits—apples, bananas, and oranges—all grow on trees; therefore, you assume that all fruit must grow on trees. This would be an example of inductive reasoning, and, clearly, the existence of strawberries, blueberries, and kiwi demonstrate that this generalization is not correct despite it being based on a number of direct observations. Scientists use inductive reasoning to formulate theories, which in turn generate hypotheses that are tested with deductive reasoning. In the end, science involves both deductive and inductive processes.

For example, case studies, which you will read about in the next section, are heavily weighted on the side of empirical observations. Thus, case studies are closely associated with inductive processes as researchers gather massive amounts of observations and seek interesting patterns (new ideas) in the data. Experimental research, on the other hand, puts great emphasis on deductive reasoning.

We’ve stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A theory is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.

A hypothesis is a testable prediction about how the world will behave if our idea is correct, and it is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests Figure 2.5 .

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later chapter, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

A scientific hypothesis is also falsifiable , or capable of being shown to be incorrect. Recall from the introductory chapter that Sigmund Freud had lots of interesting ideas to explain various human behaviors ( Figure 2.6 ). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Scientific research’s dependence on falsifiability allows for great confidence in the information that it produces. Typically, by the time information is accepted by the scientific community, it has been tested repeatedly.

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2.1.4: Null Hypothesis Significance Testing

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Null Hypotheses and Research Hypotheses

So far, so good? We develop a directional research hypothesis that names our groups, the DV (the outcome that was measured), and indicates a direction (which group will be higher). And we have a null hypothesis that says that the groups will have similar means on the DV. It’s at this point that things get somewhat counterintuitive. Because the null hypothesis seems to correspond to the opposite of what I want to believe, and then we focus exclusively on that, almost to the neglect of the thing I’m actually interested in (the research hypothesis). In our growth mindset example, the null hypothesis is that the sample of junior high students with high beliefs in growth mindset will have similar average study times compared to the population of all junior high students. But for Blackwell, Trzseniewski, and Dweck (2007), and, really, any teacher ever, we actually want to believe that the understanding that your intelligence and abilities can always improve (high belief in growth mindset) will result in working harder and spending more time on homework. So the alternative to this null hypothesis is that those junior high students with higher growth mindset scores will spend more time on their math homework than those from the population of junior high students. The important thing to recognize is that the goal of a hypothesis test is not to show that the research hypothesis is (probably) true; the goal is to show that the null hypothesis is (probably) false. Most people find this pretty weird.

The best way to think about it, in my experience, is to imagine that a hypothesis test is a criminal trial… the trial of the null hypothesis . The null hypothesis is the defendant, the researcher is the prosecutor, and the statistical test itself is the judge. Just like a criminal trial, there is a presumption of innocence: the null hypothesis is deemed to be true unless you, the researcher, can prove beyond a reasonable doubt that it is false. You are free to design your experiment however you like, and your goal when doing so is to maximize the chance that the data will yield a conviction… for the crime of being false. The catch is that the statistical test sets the rules of the trial, and those rules are designed to protect the null hypothesis – specifically to ensure that if the null hypothesis is actually true, the chances of a false conviction are guaranteed to be low. This is pretty important: after all, the null hypothesis doesn’t get a lawyer. And given that the researcher is trying desperately to prove it to be false, someone has to protect it.

Okay, so the null hypothesis always states that there's no difference. In our examples so far, we've been saying that there's no difference between the sample mean and population mean. But we don’t really expect that, or why would we be comparing the means? The purpose of null hypothesis significance testing is to be able to reject the expectation that the means of the two groups are the same .

• Rejecting the null hypothesis means that \( \bar{X} \neq \mu \).
• Rejecting the null hypothesis doesn't automatically mean that the research hypothesis is supported.
• Retaining the null hypothesis means that \( \bar{X} = \mu \).
• This means that our research hypothesis cannot be true.

We only reject or retain the null hypothesis. If we reject the null hypothesis (which says that everything is similar), we are saying that some means are statistically different from some other means. We only support the research hypothesis if the means are in the direction that we said. For example, if we rejected the null hypothesis that junior highers with high growth mindset spend as much time on homework as all junior highers, we can't automatically say that junior high students with high growth mindset study more than the population of junior high students. Instead, we'd have to look at the actual means of each group, and then decide if the research hypothesis was supported or not.

I hope that it's obvious that you don't have to look at the group means if the null hypothesis is retained?

In sum, you reject or retain the null hypothesis, and your support or or don’t support the research hypothesis.

Why predict that two things are similar?

Because each sample’s mean will vary around the population mean (see the first few sections of this chapter to remind yourself of this), we can’t tell if our sample’s mean is within a “normal” variance. But we can gather data to show that this sample’s mean is different (enough) from the population’s mean. This is rejecting the null hypothesis .

We use statistics to determine the probability of the null hypothesis being true.

Exercise \(\PageIndex{1}\)

Does a true null hypothesis say the sample mean and the population mean are similar or different?

A null hypothesis always says that the means are similar (or that there is no relationship between the variables).

Why can’t we prove that the mean of our sample is different from the mean of the population? Remember the first few sections of this chapter, that showed how different samples from the same population have different means and standard deviations. Researchers are a conservative bunch; we don't want to stake our reputation on a sample mean that could be fluke, one of the extreme handfuls of green gumballs even when the mean difference between hands was zero.

But what we can show is that our sample is so extreme that it is statistically unlikely to be similar to the population.

Null hypothesis significant testing is like how courts decide if defendants are Guilty or Not Guilty, not their Guilt v. Innocent. Similarly, we decide if the sample is similar to the population or not.

This is a tough concept to grasp, so we'll keep working on it. And if you never get it, that's okay, too, as long as you remember the pattern of rejecting or retaining the null hypothesis, and supporting or not supporting the research hypothesis.

Model Answers

Q: Discuss the importance and sources of hypothesis in social research.

Question asked in UPSC Sociology 2020 Paper 1. Download our app for last 20 year question with model answers.

Model Answer:

Importance of Hypothesis in Social Research

Hypothesis in social research refers to a tentative statement or assumption about the relationship between two or more variables. It is a testable prediction that serves as a starting point for conducting a study. Hypotheses are important in social research for several reasons:

1. Direction and focus: Hypotheses provide a clear direction and focus for the research. They help researchers identify the variables that need to be studied and the relationships that need to be explored. For example, a hypothesis might state that “ higher levels of education lead to higher income levels. ” This statement provides a clear direction for the researcher to investigate the relationship between education and income.

2. Basis for research design: Hypotheses serve as the foundation for designing a research study. They help researchers choose the appropriate methods, techniques, and tools to collect and analyze data. For instance, if a researcher wants to test the hypothesis that “ participation in sports reduces the likelihood of engaging in criminal behavior, ” they might design a study that compares crime rates among individuals who participate in sports and those who do not.

3. Testability: Hypotheses are testable statements that can be either supported or refuted by empirical evidence. This testability is crucial for the scientific process, as it allows researchers to build on existing knowledge and contribute to the understanding of social phenomena. For example, if a hypothesis states that “ social media use increases feelings of loneliness, ” researchers can collect data on social media usage and loneliness levels to test this assumption.

4. Explanation and prediction: Hypotheses help researchers explain and predict social phenomena. By identifying relationships between variables, hypotheses can provide insights into the underlying mechanisms and processes that drive social behavior. For instance, a hypothesis that “ unemployment leads to increased crime rates, ” might suggest that addressing unemployment could help reduce crime.

Sources of hypothesis in social research:

1. Theory: Hypotheses can be derived from existing theories in the field. Theories provide a framework for understanding social phenomena and can suggest relationships between variables that can be tested through research. For example, social learning theory might suggest the hypothesis that “ children who witness violence in their homes are more likely to exhibit aggressive behavior. “

2. Previous research: Hypotheses can be based on the findings of previous studies. Researchers can build on existing knowledge by testing new relationships or exploring the same relationships in different contexts. For example, if a previous study found a relationship between poverty and crime in urban areas, a researcher might hypothesize that the same relationship exists in rural areas.

3. Observations and personal experiences: Researchers can develop hypotheses based on their own observations and experiences. These insights can provide a starting point for investigating social phenomena. For example, a researcher who notices a high rate of teenage pregnancy in their community might hypothesize that a lack of access to sexual education is a contributing factor.

4. Expert opinions and literature reviews: Consulting experts in the field and reviewing existing literature can help researchers identify gaps in knowledge and generate hypotheses. For instance, a review of research on the effects of social media on mental health might reveal conflicting findings, leading a researcher to hypothesize that certain factors, such as the type of social media platform or the amount of time spent online, might moderate these effects.

In conclusion, hypotheses play a crucial role in social research by providing direction, focus, and a basis for research design. They are derived from various sources, including theory, previous research, observations, and expert opinions. By testing hypotheses, researchers can contribute to the understanding of social phenomena and inform policies and interventions aimed at addressing social issues.

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• Categories: Engaging with Courses , Strategies for Learning

Reading is one of the most important components of college learning, and yet it’s one we often take for granted. Of course, students who come to Harvard know how to read, but many are unaware that there are different ways to read and that the strategies they use while reading can greatly impact memory and comprehension. Furthermore, students may find themselves encountering kinds of texts they haven’t worked with before, like academic articles and books, archival material, and theoretical texts.  

So how should you approach reading in this new environment? And how do you manage the quantity of reading you’re asked to cover in college? 

Start by asking “Why am I reading this?”

To read effectively, it helps to read with a goal . This means understanding before you begin reading what you need to get out of that reading. Having a goal is useful because it helps you focus on relevant information and know when you’re done reading, whether your eyes have seen every word or not. 

Some sample reading goals:

• To find a paper topic or write a paper; 
• To have a comment for discussion; 
• To supplement ideas from lecture; 
• To understand a particular concept; 
• To memorize material for an exam; 
• To research for an assignment; 
• To enjoy the process (i.e., reading for pleasure!). 

Your goals for reading are often developed in relation to your instructor’s goals in assigning the reading, but sometimes they will diverge. The point is to know what you want to get out of your reading and to make sure you’re approaching the text with that goal in mind. Write down your goal and use it to guide your reading process. 

Next, ask yourself “How should I read this?”  

Not every text you’re assigned in college should be read the same way.  Depending on the type of reading you’re doing and your reading goal, you may find that different reading strategies are most supportive of your learning. Do you need to understand the main idea of your text? Or do you need to pay special attention to its language? Is there data you need to extract? Or are you reading to develop your own unique ideas?  

The key is to choose a reading strategy that will help you achieve your reading goal. Factors to consider might be: 

• The timing of your reading (e.g., before vs. after class) 
• What type of text you are reading (e.g., an academic article vs. a novel) 
• How dense or unfamiliar a text is 
• How extensively you will be using the text 
• What type of critical thinking (if any) you are expected to bring to the reading 

Based on your consideration of these factors, you may decide to skim the text or focus your attention on a particular portion of it. You also might choose to find resources that can assist you in understanding the text if it is particularly dense or unfamiliar. For textbooks, you might even use a reading strategy like SQ3R .

Finally, ask yourself “How long will I give this reading?”  

Often, we decide how long we will read a text by estimating our reading speed and calculating an appropriate length of time based on it. But this can lead to long stretches of engaging ineffectually with texts and losing sight of our reading goals. These calculations can also be quite inaccurate, since our reading speed is often determined by the density and familiarity of texts, which varies across assignments. 

For each text you are reading, ask yourself “based on my reading goal, how long does this reading deserve ?” Sometimes, your answer will be “This is a super important reading. So, it takes as long as it takes.” In that case, create a time estimate using your best guess for your reading speed. Add some extra time to your estimate as a buffer in case your calculation is a little off. You won’t be sad to finish your reading early, but you’ll struggle if you haven’t given yourself enough time. 

For other readings, once we ask how long the text deserves, we will realize based on our other academic commitments and a text’s importance in the course that we can only afford to give a certain amount of time to it. In that case, you want to create a time limit for your reading. Try to come up with a time limit that is appropriate for your reading goal. For instance, let’s say I am working with an academic article. I need to discuss it in class, but I can only afford to give it thirty minutes of time because we’re reading several articles for that class. In this case, I will set an alarm for thirty minutes and spend that time understanding the thesis/hypothesis and looking through the research to look for something I’d like to discuss in class. In this case, I might not read every word of the article, but I will spend my time focusing on the most important parts of the text based on how I need to use it. 

If you need additional guidance or support, reach out to the course instructor and the ARC.  

If you find yourself struggling through the readings for a course, you can ask the course instructor for guidance. Some ways to ask for help are: “How would you recommend I go about approaching the reading for this course?” or “Is there a way for me to check whether I am getting what I should be out of the readings?” 

If you are looking for more tips on how to read effectively and efficiently, book an appointment with an academic coach at the ARC to discuss your specific assignments and how you can best approach them! 

SQ3R is a form of reading and note taking that is especially suited to working with textbooks and empirical research articles in the sciences and social sciences. It is designed to facilitate your reading process by drawing your attention to the material you don’t know, while building on the pre-existing knowledge you already have. It’s a great first step in any general study plan. Here are the basic components:

When using SQ3R, you don’t start by reading, but by “surveying” the text as a whole. What does that mean? Surveying involves looking at all the components of the text—like its subheadings, figures, review questions, etc.—to get a general sense of what the text is trying to achieve. 

The next step of SQ3R still doesn’t involve reading! Now your job is to create questions around the material you noted in your survey. Make note of the things you already seem to understand even without reading, and then write out questions about the material that seems new or that you don’t fully understand. This list of questions will help guide your reading, allowing you to focus on what you need to learn about the topic. The goal is to be able to answer these questions by the end of your reading (and to use them for active study as well!). 

Now that you’ve surveyed and questioned your text, it’s finally time to read! Read with an eye toward answering your questions, and highlight or make marginal notes to yourself to draw your attention to important parts of the text. 

If you’ve read your text with an eye to your questions, you will now want to practice answering them out loud. You can also take notes on your answers. This will help you know what to focus on as you review. 

As you study, look back at your questions. You might find it helpful to move those questions off the physical text. For example, when you put questions on flashcards, you make it hard to rely on memory cues embedded on the page and, thus, push yourself to depend on your own memory for the answer. (Of course, drawing from your memory is what you’ll need to do for the test!) 

Seeing Textbooks in a New Light

Textbooks can be a fantastic supportive resource for your learning. They supplement the learning you’ll do in the classroom and can provide critical context for the material you cover there. In some courses, the textbook may even have been written by the professor to work in harmony with lectures.  

There are a variety of ways in which professors use textbooks, so you need to assess critically how and when to read the textbook in each course you take.  

Textbooks can provide: 

• A fresh voice through which to absorb material. For challenging concepts, they can offer new language and details that might fill in gaps in your understanding. 
• The chance to “preview” lecture material, priming your mind for the big ideas you’ll be exposed to in class. 
• The chance to review material, making sense of the finer points after class. 
• A resource that is accessible any time, whether it’s while you are studying for an exam, writing a paper, or completing a homework assignment.

Textbook reading is similar to and different from other kinds of reading . Some things to keep in mind as you experiment with its use: 

The answer is “both” and “it depends.” In general, reading or at least previewing the assigned textbook material before lecture will help you pay attention in class and pull out the more important information from lecture, which also tends to make note-taking easier. If you read the textbook before class, then a quick review after lecture is useful for solidifying the information in memory, filling in details that you missed, and addressing gaps in your understanding. In addition, reading before and/or after class also depends on the material, your experience level with it, and the style of the text. It’s a good idea to experiment with when works best for you!

 Just like other kinds of course reading, it is still important to read with a goal . Focus your reading goals on the particular section of the textbook that you are reading: Why is it important to the course I’m taking? What are the big takeaways? Also take note of any questions you may have that are still unresolved.

Reading linearly (left to right and top to bottom) does not always make the most sense. Try to gain a sense of the big ideas within the reading before you start: Survey for structure, ask Questions, and then Read – go back to flesh out the finer points within the most important and detail-rich sections.

Summarizing pushes you to identify the main points of the reading and articulate them succinctly in your own words, making it more likely that you will be able to retrieve this information later. To further strengthen your retrieval abilities, quiz yourself when you are done reading and summarizing. Quizzing yourself allows what you’ve read to enter your memory with more lasting potential, so you’ll be able to recall the information for exams or papers. 

Marking Text

Marking text, which often involves making marginal notes, helps with reading comprehension by keeping you focused. It also helps you find important information when reviewing for an exam or preparing to write an essay. The next time you’re reading, write notes in the margins as you go or, if you prefer, make notes on a separate document. 

Your marginal notes will vary depending on the type of reading. Some possible areas of focus: 

• What themes do you see in the reading that relate to class discussions? 
• What themes do you see in the reading that you have seen in other readings? 
• What questions does the reading raise in your mind? 
• What does the reading make you want to research more? 
• Where do you see contradictions within the reading or in relation to other readings for the course? 
• Can you connect themes or events to your own experiences? 

Your notes don’t have to be long. You can just write two or three words to jog your memory. For example, if you notice that a book has a theme relating to friendship, you can just write, “pp. 52-53 Theme: Friendship.” If you need to remind yourself of the details later in the semester, you can re-read that part of the text more closely.

Reading Workshops

If you are looking for help with developing best practices and using strategies for some of the tips listed above, come to an ARC workshop on reading!

• Open access
• Published: 05 June 2024

Current status and ongoing needs for the teaching and assessment of clinical reasoning – an international mixed-methods study from the students` and teachers` perspective

• F. L Wagner 1 ,
• M. Sudacka 2 ,
• A. A Kononowicz 3 ,
• M. Elvén 4 , 5 ,
• S. J Durning 6 ,
• I. Hege 7 &
• S. Huwendiek 1  

BMC Medical Education volume  24 , Article number:  622 ( 2024 ) Cite this article

46 Accesses

Metrics details

Clinical reasoning (CR) is a crucial ability that can prevent errors in patient care. Despite its important role, CR is often not taught explicitly and, even when it is taught, typically not all aspects of this ability are addressed in health professions education. Recent research has shown the need for explicit teaching of CR for both students and teachers. To further develop the teaching and learning of CR we need to improve the understanding of students' and teachers' needs regarding content as well as teaching and assessment methods for a student and trainer CR curriculum.

Parallel mixed-methods design that used web-surveys and semi-structured interviews to gather data from both students (n survey  = 100; n interviews  = 13) and teachers (n survey  = 112; n interviews  = 28). The interviews and surveys contained similar questions to allow for triangulation of the results. This study was conducted as part of the EU-funded project DID-ACT ( https://did-act.eu ).

Both the surveys and interview data emphasized the need for content in a clinical reasoning (CR) curriculum such as “gathering, interpreting and synthesizing patient information”, “generating differential diagnoses”, “developing a diagnostic and a treatment plan” and “collaborative and interprofessional aspects of CR”. There was high agreement that case-based learning and simulations are most useful for teaching CR. Clinical and oral examinations were favored for the assessment of CR. The preferred format for a train-the-trainer (TTT)-course was blended learning. There was also some agreement between the survey and interview participants regarding contents of a TTT-course (e.g. teaching and assessment methods for CR). The interviewees placed special importance on interprofessional aspects also for the TTT-course.

Conclusions

We found some consensus on needed content, teaching and assessment methods for a student and TTT-course in CR. Future research could investigate the effects of CR curricula on desired outcomes, such as patient care.

Peer Review reports

Introduction

Clinical reasoning (CR) is a universal ability that mobilizes integration of necessary fundamental knowledge while delivering high-quality patient care in a variety of contexts in a timely and effective way [ 1 , 2 ]. Daniel et al. [ 3 ] define it as a “skill, process or outcome wherein clinicians observe, collect, and interpret data to diagnose and treat patients”. CR encompasses health professionals thinking and acting in patient assessment, diagnostic, and management processes in clinical situations, taking into account the patient ‘s specific circumstances and preferences [ 4 ]. How CR is defined can vary between health professions, but there are also similarities [ 5 ]. Poor CR is associated with low-quality patient care and increases the risk of medical errors [ 6 ]. Berner and Graber [ 7 ] suggested that the rate of diagnostic error is around 15%, underlining the threat that insufficient CR ability poses to patient safety as well as increasing healthcare costs [ 8 ]. Despite the importance of CR, it appears to be rarely taught or assessed explicitly, often only parts of the CR process are covered in existing curricula, and there seems to be a lack of progression throughout curricula (e.g. [ 9 , 10 , 11 , 12 , 13 , 14 ].). Moreover, teachers are often not trained to explicitly teach CR, including explaining their own reasoning to others [ 10 , 11 , 12 ] although this appears to be an important factor in the implementation of a CR curriculum [ 15 ]. Some teachers even question whether CR can be explicitly taught [ 16 ]. Considering these findings, efforts should be made to incorporate explicit teaching of CR into health care professions curricula and training for teachers should be established based on best evidence. However, to date, little is known about what a longitudinal CR curriculum should incorporate to meet the needs of teachers and students.

Insights regarding teaching CR were provided from a global survey by Kononowicz et al. [ 10 ], who reported a need for a longitudinal CR curriculum. However, the participants in their study were mainly health professions educators, leaving the needs of students for a CR curriculum largely unknown. As students are future participants of a CR curriculum, their needs should also be investigated. Kononowicz et al. [ 10 ] also identified a lack of qualified faculty to teach CR. A train-the-trainer course for CR could help reduce this barrier to teaching CR. To the best of our knowledge, in addition to the work by Kononowicz et al. [ 10 ], no research exists yet that addresses the needs of teachers for such a course, and Kononowicz et al. [ 10 ] did not investigate their needs beyond course content. Recently, Gupta et al. [ 12 ] and Gold et al. [ 13 ] conducted needs analyses regarding clinical reasoning instruction from the perspective of course directors at United States medical schools, yet a European perspective is missing. Thus, our research questions were the following:

What aspects of clinical reasoning are currently taught and how important are they in a clinical reasoning curriculum according to teachers and students?

What methods are currently used to teach and assess clinical reasoning and which methods would be ideal according to teachers and students?

In what study year does the teaching of clinical reasoning currently begin and when should it ideally begin according to teachers and students?

How should a train-the-trainer course for teachers of clinical reasoning be constructed regarding content and format?

In this study, we used a convergent parallel mixed-methods design [ 17 ] within a pragmatic constructivist case study approach [ 18 ]. We simultaneously collected data from students and educators using online questionnaires and semi-structured interviews to gain deeper insight into their needs on one particular situation [ 19 ]– the development of a clinical reasoning curriculum—to address our research questions. To help ensure that the results of the survey and the interviews could be compared and integrated, we constructed the questions for the survey and the interviews similarly with the exception that in the interviews, the questions were first asked openly. The design was parallel both in that we collected data simultaneously and also constructed the survey and interviews to cover similar topics. We chose this approach to obtain comprehensive answers to the research questions and to facilitate later triangulation [ 17 ] of the results.

Context of this study

We conducted this study within the EU-funded (Erasmus + program) project DID-ACT (“Developing, implementing, and disseminating an adaptive clinical reasoning curriculum for healthcare students and educators”; https://did-act.eu ). Institutions from six European countries (Augsburg University, Germany; Jagiellonian University in Kraków, Poland; Maribor University, Slovenia; Örebro University, Sweden; University of Bern, Switzerland; EDU, a higher medical education institution based in Malta, Instruct GmbH, Munich, Germany) with the support of associate partners (e.g., Prof. Steven Durning, Uniformed Services University of the Health Sciences, USA; Mälardalen University, Sweden.) were part of this project. For further information, see https://did-act.eu/team-overview/team/ . In this project, we developed an interprofessional longitudinal clinical reasoning curriculum for students in healthcare education and a train-the-trainer course for health profession educators. The current curriculum (for a description of the curriculum, see Hege et al. [ 20 ]) was also informed by this study. This study was part of the Erasmus + Knowledge Alliance DID-ACT (612,454-EPP-1–2019-1-DE-EPPKA2-KA).

Target groups

We identified two relevant target groups for this study, teachers and students, which are potential future users and participants of a train—the—trainer (TTT-) course and a clinical reasoning curriculum, respectively. The teacher group also included individuals who were considered knowledgeable regarding the current status of clinical reasoning teaching and assessment at their institutions (e.g. curriculum managers). These specific participants were individually selected by the DID-ACT project team to help ensure that they had the desired level of expertise. The target groups included different health professions from a large number of countries (see Table  1 ), as we wanted to gather insights that are not restricted to one profession.

Development of data collection instruments

Development of questions.

The questions in this study addressed the current status and needs regarding content, teaching, and assessment of clinical reasoning (CR). They were based on the questions used by Kononowicz et al. [ 10 ] and were expanded to obtain more detailed information. Specifically, regarding CR content, we added additional aspects (see Table 8 in the Appendix for details). The contents covered in this part of the study also align with the five domains of CR education (clinical reasoning concepts, history and physical examination, choosing and interpreting diagnostic tests, problem identification and management and shared decision-making) that were reported by Cooper et al. [ 14 ]. It has been shown that there are similarities between professions regarding the definition of CR (e.g. history taking or an emphasis on clinical skills), while nurses placed greater importance on a patient-centered approach [ 5 ]. We aimed to cover as many aspects of CR in the contents as possible to represent these findings. We expanded the questions on CR teaching formats to cover a broader range of formats. Furthermore, two additional assessment methods were added to the respective questions. Finally, one aspect was added to the content questions for a train-the-trainer course (see Table 8 in the Appendix ). As a lack of qualified faculty to teach CR was identified in the study by Kononowicz et al. [ 10 ], we added additional questions on the specific needs for the design of a CR train-the-trainer course beyond content. Table 8 in the Appendix shows the adaptations that we made in detail.

We discussed the questions within the interprofessional DID-ACT project team and adapted them in several iterative cycles until the final versions of the survey questionnaire and the interview guide were obtained and agreed upon. We tested the pre-final versions with think-alouds [ 21 ] to ensure that the questions were understandable and interpreted as intended, which led to a few changes. The survey questionnaires and interview-guides can be found at https://did-act.eu/results/ and accessed via links in table sections D1.1a (survey questions) and D1.1b (interview guides), respectively. Of these questions, we included only those relevant to the research questions addressed in this study. The questions included in this study can be found in the Appendix in Table8.

Teachers were asked questions about all content areas, but only the expert subgroup was asked to answer questions on the current situation regarding the teaching and assessment of clinical reasoning at their institutions, as they were considered the best informed group on the matter. Furthermore, students were not asked questions on the train-the-trainer course. Using the abovementioned procedures, we also hoped to improve the response rate as longer surveys were found to be associated with lower response rates [ 22 ].

We created two different versions of the interview guide, one for teachers and one for students. The student interview guide did not contain questions on the current status of clinical reasoning teaching and assessment or questions about the train-the-trainer course. The interview guides were prepared with detailed instructions to ensure that the interviews were conducted in a comparable manner at all locations. By using interviews, we intended to obtain a broad picture of existing needs. Individual interviews further allowed participants to speak their own languages and thus to express themselves naturally and as precisely as possible.

Reflexivity statement

Seven researchers representing different perspectives and professions form the study team. MS has been a PhD candidate representing the junior researcher perspective, while also experienced researchers with a broad background in clinical reasoning and qualitative as well as quantitative research are part of the team (SD, SH, AK, IH, ME, FW). ME represents the physiotherapist perspective, SD, SH, and MS represent the medical perspective. We discussed all steps of the study in the team and made joint decisions.

Data collection and analysis

The survey was created using LimeSurvey software (LimeSurvey GmbH). The survey links were distributed via e-mail (individual invitations, posts to institutional mailing lists, newsletters) by the DID-ACT project team and associate partners (the target groups received specific links to the online-survey). The e-mail contained information on the project and its goals. By individually contacting persons in the local language, we hoped to increase the likelihood of participation. The survey was anonymous. The data were collected from March to July 2020.

Potential interview participants were contacted personally by the DID-ACT project team members in their respective countries. We used a convenience sampling approach by personally contacting potential interview partners in the local language to motivate as many participants as possible. With this approach we also hoped to increase the likelihood of participation. The interviews were conducted in the local languages also to avoid language barriers and were audio-recorded to help with the analysis and for documentation purposes. Most interviews were conducted using online meeting services (e.g. Skype or Zoom) because of restrictions due to the ongoing coronavirus pandemic that occurred with the start of data collection at the beginning of the DID-ACT project. The data were collected from March to July 2020. All interview partners provided informed consent.

Ethics approval and consent to participate

We asked the Bern Ethics Committee to approve this multi-institutional study. This type of study was regarded as exempt from formal ethical approval according to the regulations of the Bern Ethics Committee (‘Kantonale Ethikkommission Bern’, decision Req-2020–00074). All participants voluntarily participated and provided informed consent before taking part in this study.

Data analysis

Descriptive analyses were performed using SPSS statistics software (version 28, 2021). Independent samples t-tests were computed for comparisons between teachers and students. When the variances of the two groups were unequal, Welch’s test was used. Bonferroni correction of significance levels was used to counteract alpha error accumulation in repeated tests. The answers to the free text questions were screened for recurring themes. There were very few free-text comments, typically repeating aspects from the closed questions, hence, no meaningful analysis was possible. For this reason, the survey comments are mentioned only where they made a unique contribution to the results.

The interviews were translated into English by the partners. An overarching summarizing qualitative content analysis [ 23 ] of the data was conducted. A summarizing content analysis is particularly useful when the content level of the material is of interest. Its goal is to reduce the material to manageable short texts in a way that retains the essential meaning [ 23 ]. The analysis was conducted first by two of the authors of the study (FW, SH) and then discussed by the entire author team. The analysis was carried out as an iterative process until a complete consensus was reached within the author team.

The results from the surveys and interviews were compared and are presented together in the results section. The qualitative data are reported in accordance with the standards for reporting qualitative research (SRQR, O’Brien et al. [ 24 ]).

Table 1 shows the professional background and country of the interviewees and survey samples. The survey was opened by 857 persons, 212 (25%) of whom answered the questions included in this study. The expert sub-group of teachers who answered the questions on the current status of clinical reasoning teaching and assessment encompassed 45 individuals.

Content of a clinical reasoning curriculum for students

The survey results show that “Gathering, interpreting, and synthesizing patient information”, is currently most extensively taught, while “Theories of clinical reasoning” are rarely taught (see Table  2 ). In accordance with these findings, “Gathering, interpreting, and synthesizing patient information” received the highest mean importance rating for a clinical reasoning curriculum while “Theories of clinical reasoning” received the lowest importance rating. Full results can be found in Table 9 in the Appendix .

Teachers and students differed significantly in their importance ratings of two content areas, “Gathering, interpreting, and synthesizing patient information” ( t (148.32) = 4.294, p  < 0.001, d  = 0.609) and “Developing a problem formulation/hypothesis” ( t (202) = 4.006, p  < 0.001, d  = 0.561), with teachers assigning greater importance to both of these content areas.

The results from the interviews are in line with those from the survey. Details can be found in Table 12 in the Appendix .

Clinical reasoning teaching methods

The survey participants reported that, most often, case-based learning is currently applied in the teaching of clinical reasoning (CR). This format was also rated as most important for teaching CR (see Table  3 ). Full results can be found in Table 10 in the Appendix .

Teachers and students differed significantly in their importance ratings of Team-based learning ( t (202) = 3.079, p  = 0.002, d  = 0.431), with teachers assigning greater importance to this teaching format.

Overall, the interviewees provided very similar judgements to the survey participants. Next to the teaching formats shown in Table  3 , some of them would employ blended learning, and clinical teaching formats such as bedside teaching and internships were also mentioned. Details can be found in the Appendix in Table 13. In addition to the importance of each individual teaching format, it was also argued that all of the formats can be useful because they all are meant to reach different objectives and that there is not one single best format for teaching CR.

Start of clinical reasoning teaching in curricula

Most teachers (52.5%) reported that currently, the teaching of clinical reasoning (CR) starts in the first year of study. Most often (46.4%) the participants also chose the first study year as the optimal year for starting the teaching CR. In accordance with the survey results, the interviewees also advocated for an early start of the teaching of CR. Some interview participants who advocated for a later start of CR teaching suggested that the students first need a solid knowledge base and that once the clinical/practical education starts, explicit teaching of CR should begin.

Assessment of clinical reasoning

The survey results suggest that currently written tests or clinical examinations are most often used, while Virtual Patients are used least often (see Table  4 ). Despite written tests being the most common current assessment format, they received the lowest importance rating for a future longitudinal CR curriculum. Full results can be found in Table 11 in the Appendix .

Teachers and students differed significantly in their importance ratings of clinical examinations ( t (161.81) = 2.854, p  = 0.005, d  = 0.413) and workplace-based assessments ( t (185) = 2.640, p = 0.009, d  = 0.386) with teachers assigning greater importance to both of these assessment formats.

The interviewees also placed importance on all assessment methods but found it difficult to assess CR with written assessment methods. The students seemed to associate clinical examinations more with practical skills than with CR. Details can be found in the Appendix in Table 14. Two of the interview participants mentioned that CR is currently not assessed at their institutions, and one person mentioned that students are asked to self-reflect on their interactions with patients and on potential improvements.

Train-the-trainer course

The following sections highlight the results from the needs analysis regarding a train-the-trainer (TTT-) course. The questions presented here were posed only to the teachers.

Most survey participants reported that there is currently no TTT- course on clinical reasoning at their institution but that they think such a course is necessary (see Table  5 ). The same was also true for the interviewees (no TTT- course on clinical reasoning existing but need for one).

In the interviews, 22 participants (78.6%) answered that a TTT-course is necessary for healthcare educators, two participants answered that no such course was necessary, and two other participants were undecided about its necessity. At none of the institutions represented by the interviewees, a TTT-course for teaching clinical reasoning exists.

When asked what the best format for a clinical reasoning TTT- course would be (single answer question), the majority of the survey participants favored a blended learning / flipped classroom approach, a combination of e-learning and face-to-face meetings. (see Table  6 ).

In the survey comments it was noted that blended-learning encompasses the benefits of both self-directed learning and discussion/learning from others. It would further allow teachers to gather knowledge about CR first in an online learning phase where they can take the time they need before coming to a face-to-face meeting.

The interviewees also found a blended-learning approach particularly suitable for a TTT-course. An e-learning course only was seen as more critical because teachers may lack motivation to participate in an online-only setting, while a one-time face-to-face meeting would not provide enough time. In some interviews, it was emphasized that teachers should experience themselves what they are supposed to teach to the students and also that the trainers for the teachers need to have solid education and knowledge on clinical reasoning.

Table 7 shows the importance ratings of potential content of a TTT-course generated from the survey. To elaborate on this content, comments by the interviewees were added. On average, all content was seen as (somewhat) important with teaching methods on the ward and/or clinic receiving the highest ratings. Some interviewees also mentioned the importance of interprofessional aspects and interdisciplinary understanding of CR. In the survey comments, some participants further expressed their interest in such a course.

Finally, the interviewees were asked about the ideal length of a clinical reasoning TTT-course. The answers varied greatly from 2–3 hours to a two-year educational program, with a tendency toward 1–2 days. Several interviewees commented that the time teachers are able to spend on a TTT-course is limited. This should be considered in the planning of such a course to make participation feasible for teachers.

In this study, we investigated the current status of and suggestions for teaching and assessment of clinical reasoning (CR) in a longitudinal curriculum as well as suggestions for a train-the-trainer (TTT-) course for CR. Teachers and students were invited to participate in online-surveys as well as semi-structured interviews to derive answers to our research questions. Regarding the contents of a CR curriculum for students, the results of the surveys and interviews were comparable and favoured content such as gathering, interpreting, and synthesizing patient information, generating differential diagnoses, and developing a diagnostic and a treatment plan. In the interviews, high importance was additionally placed on collaborative and interprofessional aspects of CR. Case-based learning and simulations were seen as the most useful methods for teaching CR, and clinical and oral examinations were favoured for the assessment of CR. The preferred format for a TTT-course was blended learning. In terms of course content, teaching and assessment methods for CR were emphasized. In addition to research from the North American region [ 11 ], this study provides results from predominantly European countries that support the existing findings.

Content of a clinical reasoning curriculum

Our results revealed that there are still aspects of clinical reasoning (CR), such as “Errors in the clinical reasoning process and strategies to avoid them” or “Interprofessional aspects of CR” that are rarely taught despite their high importance, corroborating the findings of Kononowicz et al. [ 10 ]. According to the interviewees, students should have basic knowledge of CR before they are taught about errors in the CR process and strategies to avoid them. The lack of teaching of errors in CR may also stem from a lack of institutional culture regarding how to manage failures in a constructive way (e.g. [ 16 , 25 ]), making it difficult to explicitly address errors and strategies to avoid them. Although highly relevant in the everyday practice of healthcare professions and underpinned by CR theoretical frameworks (e.g., distributed cognition [ 26 ]), interprofessional and collaborative aspects of CR are currently rarely considered in the teaching of CR. The interviews suggested that hierarchical distance and cultural barriers may contribute to this finding. Sudacka et al. [ 16 ] also reported cultural barriers as one reason for a lack of CR teaching. Generally, the interviewees seemed to place greater importance on interprofessional and collaborative aspects than did the survey-participants This may have been due to differences in the professions represented in the two modalities (e.g., a greater percentage of nurses among the interview participants, who tend to define CR more broadly than physicians [ 5 ]).

“Self-reflection on clinical reasoning performance and strategies for future improvement”, “Developing a problem formulation/hypothesis” and “Aspects of patient-participation in CR” were rated as important but are currently rarely taught, a finding not previously reported. The aspect “Self-reflection on clinical reasoning performance and strategies for future improvement”, received high importance ratings, but only 25% of the survey-participants answered that it is currently taught to a great extent. The interviewees agreed that self-reflection is important and added that ideally, it should be guided by specific questions. Ogdie et al. [ 27 ] found that reflective writing exercises helped students identify errors in their reasoning and biases that contributed to these errors.

“Gathering, interpreting, and synthesizing patient information” and “Developing a problem formulation/hypothesis” were rated significantly more important by teachers than by students. It appears that students may be less aware yet of the importance of gathering, interpreting, and synthesizing patient information in the clinical reasoning process. There was some indication in the interviews that the students may not have had enough experience yet with “Developing a problem formulation/hypothesis” or associate this aspect with research, possibly contributing to the observed difference.

Overall, our results on the contents of a CR curriculum suggest that all content is important and should be included in a CR curriculum, starting with basic theoretical knowledge and data gathering to more advanced aspects such as errors in CR and collaboration. Two other recent surveys conducted in the United States among pre-clerkship clinical skills course directors [ 12 ] and members of clerkship organizations [ 13 ] came to similar conclusions regarding the inclusion of clinical reasoning content at various stages of medical curricula. How to fit the content into already dense study programs, however, can still be a challenge [ 16 ].

In addition to case-based learning and clinical teaching, human simulated patients and Team-based learning also received high importance ratings for teaching clinical reasoning (CR), a finding not previously reported. Lectures, on the other hand, are seen as the least important to teach CR (see also Kononowicz et al. [ 10 ]), as they mainly deliver factual knowledge according to the interviewees. High-fidelity simulations (mannequins) and Virtual Patients (VPs) are rarely used to teach CR at the moment and are rated less important compared to other teaching formats. Some interviewees see high-fidelity simulations as more useful for teaching practical skills. The lower importance rating of VPs was surprising given that this format is case-based, provides a safe environment for learning, and is described in the literature as a well-suited tool for teaching CR [ 28 , 29 ]. Considering that VPs seemed to be used less often at the institutions involved in this study, the lack of experience with this format may have led to this result.

Teachers rated Team-based learning as significantly more important for teaching clinical reasoning than students. In the interviews, many students seemed not to be familiar with Team-based learning, possibly explaining the lower ratings the students gave this format in the survey.

Taken together, our results suggest that there is not one best format for teaching all aspects of clinical reasoning but rather that the use of all teaching formats is justified depending on the specific content to be taught and goals to be achieved. However, there was agreement that a safe learning environment where no patients can be harmed is preferred for teaching clinical reasoning, and that discussions should be possible.

There was wide agreement that clinical reasoning (CR) teaching should start in the first year of study in the curriculum. However, a few participants of this study argued that students first need to develop some general knowledge before CR is taught. Rencic et al. [ 11 ] reported that according to internal medicine clerkship directors, CR should be taught throughout all years of medical school, with a particular focus during the clinical teaching years. A similar remark was made by participants in a survey among pre-clerkship clinical skills course directors by Gupta et al. [ 12 ] where the current structure of some curricula (e.g. late introduction of the pathophysiology) was regarded as a barrier to introducing CR from the first year of study on [ 12 ].

Our results show that the most important format for assessing clinical reasoning (CR) that is also currently used to the greatest extent are clinical examinations (e.g. OSCE), consistent with Kononowicz et al. [ 10 ]. The interviewees emphasized that CR should ideally be assessed in a conversation or discussion where the learners can explain their reasoning. Given this argument, all assessment formats enabling a conversation are suitable for assessing CR. This is reflected in our survey results, where assessment formats that allow for a discussion with the learner received the most favourable importance ratings, including oral examinations. In agreement with Kononowicz et al. [ 10 ], we also found that written tests are currently used most often to assess CR but are rated as least important and suitable only for the assessment of some aspects of CR. Daniel et al. [ 3 ] argued that written exams such as MCQs, where correct answers have to be selected from a list of choices, are not the best representation of real practical CR ability. Thus, there still seems to be potential for improvement in the way CR is assessed.

Teachers rated clinical examinations and workplace-based assessments significantly higher than students. Based on the interviews, the students seemed to associate clinical examinations such as OSCEs more with a focus on practical skills than CR, potentially explaining their lower ratings of this format.

What a clinical reasoning train-the-trainer course should look like

Our results show a clear need for a clinical reasoning (CR) train-the-trainer course (see also Singh et al. [ 15 ]), which currently does not exist at most institutions represented in this study, corroborating findings by Kononowicz et al. [ 10 ]. A lack of adequately trained teachers is a common barrier to the introduction of CR content into curricula [ 12 , 16 ]. According to our results such a course should follow a blended learning/flipped classroom approach or consist of a series of face-to-face meetings. A blended-learning course would combine the benefits of both self-directed learning and the possibility for trainers to discuss with and learn from their peers, which could also increase their motivation to participate in such a course. An e-learning only course or a one-time face-to-face meeting were considered insufficient. The contents “Clinical reasoning strategies” and “Common errors in the clinical reasoning process” were given greater importance for the trainer-curriculum than for the students-curriculum, possibly reflecting higher expectations of trainers as “CR experts” compared with students. There was some agreement in the interviews that ideally, the course should not be too time-consuming, with participants tending towards an overall duration of 1–2 days, considering that most teachers usually have many duties and may not be able or willing to attend the course if it were too long. Lack of time was also identified as a barrier to attending teacher training [ 12 , 13 , 16 ].

Strengths and limitations

The strengths of this study include its international and interprofessional participants. Furthermore, we explicitly included teachers and students as target groups in the same study, which enables a comparison of different perspectives. Members of the target groups not only participated in a survey but were also interviewed to gain in-depth knowledge. A distinct strength of this study is its mixed-methods design. The two data collection methods employed in parallel provided convergent results, with responses from the web survey indicating global needs and semi-structured interviews contributing to a deeper understanding of the stakeholder groups’ nuanced expectations and perspectives on CR education.

This study is limited in that most answers came from physicians, making the results potentially less generalizable to other professions. Furthermore, there were participants from a great variety of countries, with some countries overrepresented. Because of the way the survey-invitations were distributed, the exact number of recipients is unknown, making it impossible to compute an exact response rate. Also, the response rate of the survey was rather low for individuals who opened the survey. Because the survey was anonymous, it cannot completely be ruled out that some individuals participated in both interviews and survey. Finally, there could have been some language issues in the interview analysis, as the data were translated to English at the local partner institutions before they were submitted for further analysis.

Our study provides evidence of an existing need for explicit clinical reasoning (CR) longitudinal teaching and dedicated CR teacher training. More specifically, there are aspects of CR that are rarely taught that our participants believe should be given priority, such as self-reflection on clinical reasoning performance and strategies for future improvement and aspects of patient participation in CR that have not been previously reported. Case-based learning and clinical teaching methods were again identified as the most important formats for teaching CR, while lectures were considered relevant only for certain aspects of CR. To assess CR, students should have to explain their reasoning, and assessment formats should be chosen accordingly. There was also still a clear need for a CR train-the-trainer course. In addition to existing research, our results show that such a course should ideally have a blended-learning format and should not be too time-consuming. The most important contents of the train-the-trainer course were confirmed to be teaching methods, CR strategies, and strategies to avoid errors in the CR process. Examples exist for what a longitudinal CR curriculum for students and a corresponding train-the-trainer course could look like and how these components could be integrated into existing curricula (e.g. DID-ACT curriculum [ 20 ], https://did-act.eu/integration-guide/ or the described curriculum of Singh et al. [ 15 ]). Further research should focus on whether and to what extent the intended outcomes of such a curriculum are actually reached, including the potential impact on patient care.

Availability of data and materials

All materials described in this manuscript generated during the current study are available from the corresponding author on reasonable request without breaching participant confidentiality.

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Acknowledgements

We want to thank all participants of the interviews and survey who took their time to contribute to this study despite the ongoing pandemic in 2020. Furthermore, we thank the members of the DID-ACT project team who supported collection and analysis of survey and interview data.

The views expressed herein are those of the authors and not necessarily those of the Department of Defense, the Uniformed Services University or other Federal Agencies.

This study was partially supported by the Erasmus + Knowledge Alliance DID-ACT (612454-EPP-1–2019-1-DE-EPPKA2-KA).

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Wagner, F., Sudacka, M., Kononowicz, A. et al. Current status and ongoing needs for the teaching and assessment of clinical reasoning – an international mixed-methods study from the students` and teachers` perspective. BMC Med Educ 24 , 622 (2024). https://doi.org/10.1186/s12909-024-05518-8

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On the role of hypotheses in science

Harald brüssow.

1 Laboratory of Gene Technology, Department of Biosystems, KU Leuven, Leuven Belgium

Associated Data

Scientific research progresses by the dialectic dialogue between hypothesis building and the experimental testing of these hypotheses. Microbiologists as biologists in general can rely on an increasing set of sophisticated experimental methods for hypothesis testing such that many scientists maintain that progress in biology essentially comes with new experimental tools. While this is certainly true, the importance of hypothesis building in science should not be neglected. Some scientists rely on intuition for hypothesis building. However, there is also a large body of philosophical thinking on hypothesis building whose knowledge may be of use to young scientists. The present essay presents a primer into philosophical thoughts on hypothesis building and illustrates it with two hypotheses that played a major role in the history of science (the parallel axiom and the fifth element hypothesis). It continues with philosophical concepts on hypotheses as a calculus that fits observations (Copernicus), the need for plausibility (Descartes and Gilbert) and for explicatory power imposing a strong selection on theories (Darwin, James and Dewey). Galilei introduced and James and Poincaré later justified the reductionist principle in hypothesis building. Waddington stressed the feed‐forward aspect of fruitful hypothesis building, while Poincaré called for a dialogue between experiment and hypothesis and distinguished false, true, fruitful and dangerous hypotheses. Theoretical biology plays a much lesser role than theoretical physics because physical thinking strives for unification principle across the universe while biology is confronted with a breathtaking diversity of life forms and its historical development on a single planet. Knowledge of the philosophical foundations on hypothesis building in science might stimulate more hypothesis‐driven experimentation that simple observation‐oriented “fishing expeditions” in biological research.

Short abstract

Scientific research progresses by the dialectic dialogue between hypothesis building and the experimental testing of these hypotheses. Microbiologists can rely on an increasing set of sophisticated experimental methods for hypothesis testing but the importance of hypothesis building in science should not be neglected. This Lilliput offers a primer on philosophical concepts on hypotheses in science.

INTRODUCTION

Philosophy of science and the theory of knowledge (epistemology) are important branches of philosophy. However, philosophy has over the centuries lost its dominant role it enjoyed in antiquity and became in Medieval Ages the maid of theology (ancilla theologiae) and after the rise of natural sciences and its technological applications many practising scientists and the general public doubt whether they need philosophical concepts in their professional and private life. This is in the opinion of the writer of this article, an applied microbiologist, shortsighted for several reasons. Philosophers of the 20th century have made important contributions to the theory of knowledge, and many eminent scientists grew interested in philosophical problems. Mathematics which plays such a prominent role in physics and increasingly also in other branches of science is a hybrid: to some extent, it is the paradigm of an exact science while its abstract aspects are deeply rooted in philosophical thinking. In the present essay, the focus is on hypothesis and hypothesis building in science, essentially it is a compilation what philosophers and scientists thought about this subject in past and present. The controversy between the mathematical mind and that of the practical mind is an old one. The philosopher, physicist and mathematician Pascal ( 1623 –1662a) wrote in his Pensées : “Mathematicians who are only mathematicians have exact minds, provided all things are explained to them by means of definitions and axioms; otherwise they are inaccurate. They are only right when the principles are quite clear. And men of intuition cannot have the patience to reach to first principles of things speculative and conceptional, which they have never seen in the world and which are altogether out of the common. The intellect can be strong and narrow, and can be comprehensive and weak.” Hypothesis building is an act both of intuition and exact thinking and I hope that theoretical knowledge about hypothesis building will also profit young microbiologists.

HYPOTHESES AND AXIOMS IN MATHEMATICS

In the following, I will illustrate the importance of hypothesis building for the history of science and the development of knowledge and illustrate it with two famous concepts, the parallel axiom in mathematics and the five elements hypothesis in physics.

Euclidean geometry

The prominent role of hypotheses in the development of science becomes already clear in the first science book of the Western civilization: Euclid's The Elements written about 300 BC starts with a set of statements called Definitions, Postulates and Common Notions that lay out the foundation of geometry (Euclid,  c.323‐c.283 ). This axiomatic approach is very modern as exemplified by the fact that Euclid's book remained for long time after the Bible the most read book in the Western hemisphere and a backbone of school teaching in mathematics. Euclid's twenty‐three definitions start with sentences such as “1. A point is that which has no part; 2. A line is breadthless length; 3. The extremities of a line are points”; and continues with the definition of angles (“8. A plane angle is the inclination to one another of two lines in a plane which meet one another and do not lie in a straight line”) and that of circles, triangles and quadrilateral figures. For the history of science, the 23rd definition of parallels is particularly interesting: “Parallel straight lines are straight lines which, being in the same plane and being produced indefinitely in both directions, do not meet one another in either direction”. This is the famous parallel axiom. It is clear that the parallel axiom cannot be the result of experimental observations, but must be a concept created in the mind. Euclid ends with five Common Notions (“1. Things which are equal to the same thing are also equal to one another, to 5. The whole is greater than the part”). The establishment of a contradiction‐free system for a branch of mathematics based on a set of axioms from which theorems were deduced was revolutionary modern. Hilbert ( 1899 ) formulated a sound modern formulation for Euclidian geometry. Hilbert's axiom system contains the notions “point, line and plane” and the concepts of “betweenness, containment and congruence” leading to five axioms, namely the axioms of Incidence (“Verknüpfung”), of Order (“Anordnung”), of Congruence, of Continuity (“Stetigkeit”) and of Parallels.

Origin of axioms

Philosophers gave various explanations for the origin of the Euclidean hypotheses or axioms. Plato considered geometrical figures as related to ideas (the true things behind the world of appearances). Aristoteles considered geometric figures as abstractions of physical bodies. Descartes perceived geometric figures as inborn ideas from extended bodies ( res extensa ), while Pascal thought that the axioms of Euclidian geometry were derived from intuition. Kant reasoned that Euclidian geometry represented a priori perceptions of space. Newton considered geometry as part of general mechanics linked to theories of measurement. Hilbert argued that the axioms of mathematical geometry are neither the result of contemplation (“Anschauung”) nor of psychological source. For him, axioms were formal propositions (“formale Aussageformen”) characterized by consistency (“Widerspruchsfreiheit”, i.e. absence of contradiction) (Mittelstrass,  1980a ).

Definitions

Axioms were also differently defined by philosophers. In Topics , Aristoteles calls axioms the assumptions taken up by one partner of a dialogue to initiate a dialectic discussion. Plato states that an axiom needs to be an acceptable or credible proposition, which cannot be justified by reference to other statements. Yet, a justification is not necessary because an axiom is an evident statement. In modern definition, axioms are methodical first sentences in the foundation of a deductive science (Mittelstrass,  1980a ). In Posterior Analytics , Aristotle defines postulates as positions which are at least initially not accepted by the dialogue partners while hypotheses are accepted for the sake of reasoning. In Euclid's book, postulates are construction methods that assure the existence of the geometric objects. Today postulates and axioms are used as synonyms while the 18th‐century philosophy made differences: Lambert defined axioms as descriptive sentences and postulates as prescriptive sentences. According to Kant, mathematical postulates create (synthesize) concepts (Mittelstrass,  1980b ). Definitions then fix the use of signs; they can be semantic definitions that explain the proper meaning of a sign in common language use (in a dictionary style) or they can be syntactic definitions that regulate the use of these signs in formal operations. Nominal definitions explain the words, while real definitions explain the meaning or the nature of the defined object. Definitions are thus essential for the development of a language of science, assuring communication and mutual understanding (Mittelstrass,  1980c ). Finally, hypotheses are also frequently defined as consistent conjectures that are compatible with the available knowledge. The truth of the hypothesis is only supposed in order to explain true observations and facts. Consequences of this hypothetical assumptions should explain the observed facts. Normally, descriptive hypotheses precede explanatory hypotheses in the development of scientific thought. Sometimes only tentative concepts are introduced as working hypotheses to test whether they have an explanatory capacity for the observations (Mittelstrass,  1980d ).

The Euclidian geometry is constructed along a logical “if→then” concept. The “if‐clause” formulates at the beginning the supposition, the “then clause” formulates the consequences from these axioms which provides a system of geometric theorems or insights. The conclusions do not follow directly from the hypothesis; this would otherwise represent self‐evident immediate conclusions. The “if‐then” concept in geometry is not used as in other branches of science where the consequences deduced from the axioms are checked against reality whether they are true, in order to confirm the validity of the hypothesis. The task in mathematics is: what can be logically deduced from a given set of axioms to build a contradiction‐free system of geometry. Whether this applies to the real world is in contrast to the situation in natural sciences another question and absolutely secondary to mathematics (Syntopicon,  1992 ).

Pascal's rules for hypotheses

In his Scientific Treatises on Geometric Demonstrations , Pascal ( 1623‐1662b ) formulates “Five rules are absolutely necessary and we cannot dispense with them without an essential defect and frequently even error. Do not leave undefined any terms at all obscure or ambiguous. Use in definitions of terms only words perfectly well known or already explained. Do not fail to ask that each of the necessary principles be granted, however clear and evident it may be. Ask only that perfectly self‐evident things be granted as axioms. Prove all propositions, using for their proof only axioms that are perfectly self‐evident or propositions already demonstrated or granted. Never get caught in the ambiguity of terms by failing to substitute in thought the definitions which restrict or define them. One should accept as true only those things whose contradiction appears to be false. We may then boldly affirm the original statement, however incomprehensible it is.”

Kant's rules on hypotheses

Kant ( 1724–1804 ) wrote that the analysis described in his book The Critique of Pure Reason “has now taught us that all its efforts to extend the bounds of knowledge by means of pure speculation, are utterly fruitless. So much the wider field lies open to hypothesis; as where we cannot know with certainty, we are at liberty to make guesses and to form suppositions. Imagination may be allowed, under the strict surveillance of reason, to invent suppositions; but these must be based on something that is perfectly certain‐ and that is the possibility of the object. Such a supposition is termed a hypothesis. We cannot imagine or invent any object or any property of an object not given in experience and employ it in a hypothesis; otherwise we should be basing our chain of reasoning upon mere chimerical fancies and not upon conception of things. Thus, we have no right to assume of new powers, not existing in nature and consequently we cannot assume that there is any other kind of community among substances than that observable in experience, any kind of presence than that in space and any kind of duration than that in time. The conditions of possible experience are for reason the only conditions of the possibility of things. Otherwise, such conceptions, although not self‐contradictory, are without object and without application. Transcendental hypotheses are therefore inadmissible, and we cannot use the liberty of employing in the absence of physical, hyperphysical grounds of explanation because such hypotheses do not advance reason, but rather stop it in its progress. When the explanation of natural phenomena happens to be difficult, we have constantly at hand a transcendental ground of explanation, which lifts us above the necessity of investigating nature. The next requisite for the admissibility of a hypothesis is its sufficiency. That is it must determine a priori the consequences which are given in experience and which are supposed to follow from the hypothesis itself.” Kant stresses another aspect when dealing with hypotheses: “It is our duty to try to discover new objections, to put weapons in the hands of our opponent, and to grant him the most favorable position. We have nothing to fear from these concessions; on the contrary, we may rather hope that we shall thus make ourselves master of a possession which no one will ever venture to dispute.”

For Kant's analytical and synthetical judgements and Difference between philosophy and mathematics (Kant, Whitehead) , see Appendices  S1 and S2 , respectively.

Poincaré on hypotheses

The mathematician‐philosopher Poincaré ( 1854 –1912a) explored the foundation of mathematics and physics in his book Science and Hypothesis . In the preface to the book, he summarizes common thinking of scientists at the end of the 19th century. “To the superficial observer scientific truth is unassailable, the logic of science is infallible, and if scientific men sometimes make mistakes, it is because they have not understood the rules of the game. Mathematical truths are derived from a few self‐evident propositions, by a chain of flawless reasoning, they are imposed not only by us, but on Nature itself. This is for the minds of most people the origin of certainty in science.” Poincaré then continues “but upon more mature reflection the position held by hypothesis was seen; it was recognized that it is as necessary to the experimenter as it is to the mathematician. And then the doubt arose if all these constructions are built on solid foundations.” However, “to doubt everything or to believe everything are two equally convenient solutions: both dispense with the necessity of reflection. Instead, we should examine with the utmost care the role of hypothesis; we shall then recognize not only that it is necessary, but that in most cases it is legitimate. We shall also see that there are several kinds of hypotheses; that some are verifiable and when once confirmed by experiment become truths of great fertility; that others may be useful to us in fixing our ideas; and finally that others are hypotheses only in appearance, and reduce to definitions or to conventions in disguise.” Poincaré argues that “we must seek mathematical thought where it has remained pure‐i.e. in arithmetic, in the proofs of the most elementary theorems. The process is proof by recurrence. We first show that a theorem is true for n  = 1; we then show that if it is true for n –1 it is true for n; and we conclude that it is true for all integers. The essential characteristic of reasoning by recurrence is that it contains, condensed in a single formula, an infinite number of syllogisms.” Syllogism is logical argument that applies deductive reasoning to arrive at a conclusion. Poincaré notes “that here is a striking analogy with the usual process of induction. But an essential difference exists. Induction applied to the physical sciences is always uncertain because it is based on the belief in a general order of the universe, an order which is external to us. Mathematical induction‐ i.e. proof by recurrence – is on the contrary, necessarily imposed on us, because it is only the affirmation of a property of the mind itself. No doubt mathematical recurrent reasoning and physical inductive reasoning are based on different foundations, but they move in parallel lines and in the same direction‐namely, from the particular to the general.”

Non‐Euclidian geometry: from Gauss to Lobatschewsky

Mathematics is an abstract science that intrinsically does not request that the structures described reflect a physical reality. Paradoxically, mathematics is the language of physics since the founder of experimental physics Galilei used Euclidian geometry when exploring the laws of the free fall. In his 1623 treatise The Assayer , Galilei ( 1564 –1642a) famously formulated that the book of Nature is written in the language of mathematics, thus establishing a link between formal concepts in mathematics and the structure of the physical world. Euclid's parallel axiom played historically a prominent role for the connection between mathematical concepts and physical realities. Mathematicians had doubted that the parallel axiom was needed and tried to prove it. In Euclidian geometry, there is a connection between the parallel axiom and the sum of the angles in a triangle being two right angles. It is therefore revealing that the famous mathematician C.F. Gauss investigated in the early 19th century experimentally whether this Euclidian theorem applies in nature. He approached this problem by measuring the sum of angles in a real triangle by using geodetic angle measurements of three geographical elevations in the vicinity of Göttingen where he was teaching mathematics. He reportedly measured a sum of the angles in this triangle that differed from 180°. Gauss had at the same time also developed statistical methods to evaluate the accuracy of measurements. Apparently, the difference of his measured angles was still within the interval of Gaussian error propagation. He did not publish the reasoning and the results for this experiment because he feared the outcry of colleagues about this unorthodox, even heretical approach to mathematical reasoning (Carnap,  1891 ‐1970a). However, soon afterwards non‐Euclidian geometries were developed. In the words of Poincaré, “Lobatschewsky assumes at the outset that several parallels may be drawn through a point to a given straight line, and he retains all the other axioms of Euclid. From these hypotheses he deduces a series of theorems between which it is impossible to find any contradiction, and he constructs a geometry as impeccable in its logic as Euclidian geometry. The theorems are very different, however, from those to which we are accustomed, and at first will be found a little disconcerting. For instance, the sum of the angles of a triangle is always less than two right angles, and the difference between that sum and two right angles is proportional to the area of the triangle. Lobatschewsky's propositions have no relation to those of Euclid, but are none the less logically interconnected.” Poincaré continues “most mathematicians regard Lobatschewsky's geometry as a mere logical curiosity. Some of them have, however, gone further. If several geometries are possible, they say, is it certain that our geometry is true? Experiments no doubt teaches us that the sum of the angles of a triangle is equal to two right angles, but this is because the triangles we deal with are too small” (Poincaré,  1854 ‐1912a)—hence the importance of Gauss' geodetic triangulation experiment. Gauss was aware that his three hills experiment was too small and thought on measurements on triangles formed with stars.

Poincaré vs. Einstein

Lobatschewsky's hyperbolic geometry did not remain the only non‐Euclidian geometry. Riemann developed a geometry without the parallel axiom, while the other Euclidian axioms were maintained with the exception of that of Order (Anordnung). Poincaré notes “so there is a kind of opposition between the geometries. For instance the sum of the angles in a triangle is equal to two right angles in Euclid's geometry, less than two right angles in that of Lobatschewsky, and greater than two right angles in that of Riemann. The number of parallel lines that can be drawn through a given point to a given line is one in Euclid's geometry, none in Riemann's, and an infinite number in the geometry of Lobatschewsky. Let us add that Riemann's space is finite, although unbounded.” As further distinction, the ratio of the circumference to the diameter of a circle is equal to π in Euclid's, greater than π in Lobatschewsky's and smaller than π in Riemann's geometry. A further difference between these geometries concerns the degree of curvature (Krümmungsmass k) which is 0 for a Euclidian surface, smaller than 0 for a Lobatschewsky and greater than 0 for a Riemann surface. The difference in curvature can be roughly compared with plane, concave and convex surfaces. The inner geometric structure of a Riemann plane resembles the surface structure of a Euclidean sphere and a Lobatschewsky plane resembles that of a Euclidean pseudosphere (a negatively curved geometry of a saddle). What geometry is true? Poincaré asked “Ought we then, to conclude that the axioms of geometry are experimental truths?” and continues “If geometry were an experimental science, it would not be an exact science. The geometric axioms are therefore neither synthetic a priori intuitions as affirmed by Kant nor experimental facts. They are conventions. Our choice among all possible conventions is guided by experimental facts; but it remains free and is only limited by the necessity of avoiding contradictions. In other words, the axioms of geometry are only definitions in disguise. What then are we to think of the question: Is Euclidean geometry true? It has no meaning. One geometry cannot be more true than another, it can only be more convenient. Now, Euclidean geometry is, and will remain, the most convenient, 1 st because it is the simplest and 2 nd because it sufficiently agrees with the properties of natural bodies” (Poincaré,  1854 ‐1912a).

Poincaré's book was published in 1903 and only a few years later Einstein published his general theory of relativity ( 1916 ) where he used a non‐Euclidean, Riemann geometry and where he demonstrated a structure of space that deviated from Euclidean geometry in the vicinity of strong gravitational fields. And in 1919, astronomical observations during a solar eclipse showed that light rays from a distant star were indeed “bent” when passing next to the sun. These physical observations challenged the view of Poincaré, and we should now address some aspects of hypotheses in physics (Carnap,  1891 ‐1970b).

HYPOTHESES IN PHYSICS

The long life of the five elements hypothesis.

Physical sciences—not to speak of biological sciences — were less developed in antiquity than mathematics which is already demonstrated by the primitive ideas on the elements constituting physical bodies. Plato and Aristotle spoke of the four elements which they took over from Thales (water), Anaximenes (air) and Parmenides (fire and earth) and add a fifth element (quinta essentia, our quintessence), namely ether. Ether is imagined a heavenly element belonging to the supralunar world. In Plato's dialogue Timaios (Plato,  c.424‐c.348 BC a ), the five elements were associated with regular polyhedra in geometry and became known as Platonic bodies: tetrahedron (fire), octahedron (air), cube (earth), icosahedron (water) and dodecahedron (ether). In regular polyhedra, faces are congruent (identical in shape and size), all angles and all edges are congruent, and the same number of faces meet at each vertex. The number of elements is limited to five because in Euclidian space there are exactly five regular polyhedral. There is in Plato's writing even a kind of geometrical chemistry. Since two octahedra (air) plus one tetrahedron (fire) can be combined into one icosahedron (water), these “liquid” elements can combine while this is not the case for combinations with the cube (earth). The 12 faces of the dodecahedron were compared with the 12 zodiac signs (Mittelstrass,  1980e ). This geometry‐based hypothesis of physics had a long life. As late as 1612, Kepler in his Mysterium cosmographicum tried to fit the Platonic bodies into the planetary shells of his solar system model. The ether theory even survived into the scientific discussion of the 19th‐century physics and the idea of a mathematical structure of the universe dominated by symmetry operations even fertilized 20th‐century ideas about symmetry concepts in the physics of elementary particles.

Huygens on sound waves in air

The ether hypothesis figures prominently in the 1690 Treatise on Light from Huygens ( 1617‐1670 ). He first reports on the transmission of sound by air when writing “this may be proved by shutting up a sounding body in a glass vessel from which the air is withdrawn and care was taken to place the sounding body on cotton that it cannot communicate its tremor to the glass vessel which encloses it. After having exhausted all the air, one hears no sound from the metal though it is struck.” Huygens comes up with some foresight when suspecting “the air is of such a nature that it can be compressed and reduced to a much smaller space than that it normally occupies. Air is made up of small bodies which float about and which are agitated very rapidly. So that the spreading of sound is the effort which these little bodies make in collisions with one another, to regain freedom when they are a little more squeezed together in the circuit of these waves than elsewhere.”

Huygens on light waves in ether

“That is not the same air but another kind of matter in which light spreads; since if the air is removed from the vessel the light does not cease to traverse it as before. The extreme velocity of light cannot admit such a propagation of motion” as sound waves. To achieve the propagation of light, Huygens invokes ether “as a substance approaching to perfect hardness and possessing springiness as prompt as we choose. One may conceive light to spread successively by spherical waves. The propagation consists nowise in the transport of those particles but merely in a small agitation which they cannot help communicate to those surrounding.” The hypothesis of an ether in outer space fills libraries of physical discussions, but all experimental approaches led to contradictions with respect to postulated properties of this hypothetical material for example when optical experiments showed that light waves display transversal and not longitudinal oscillations.

The demise of ether

Mechanical models for the transmission of light or gravitation waves requiring ether were finally put to rest by the theory of relativity from Einstein (Mittelstrass,  1980f ). This theory posits that the speed of light in an empty space is constant and does not depend on movements of the source of light or that of an observer as requested by the ether hypothesis. The theory of relativity also provides an answer how the force of gravitation is transmitted from one mass to another across an essentially empty space. In the non‐Euclidian formulation of the theory of relativity (Einstein used the Riemann geometry), there is no gravitation force in the sense of mechanical or electromagnetic forces. The gravitation force is in this formulation simply replaced by a geometric structure (space curvature near high and dense masses) of a four‐dimensional space–time system (Carnap,  1891 ‐1970c; Einstein & Imfeld,  1956 ) Gravitation waves and gravitation lens effects have indeed been experimental demonstrated by astrophysicists (Dorfmüller et al.,  1998 ).

For Aristotle's on physical hypotheses , see Appendix  S3 .

PHILOSOPHICAL THOUGHTS ON HYPOTHESES

In the following, the opinions of a number of famous scientists and philosophers on hypotheses are quoted to provide a historical overview on the subject.

Copernicus' hypothesis: a calculus which fits observations

In his book Revolutions of Heavenly Spheres Copernicus ( 1473–1543 ) reasoned in the preface about hypotheses in physics. “Since the newness of the hypotheses of this work ‐which sets the earth in motion and puts an immovable sun at the center of the universe‐ has already received a great deal of publicity, I have no doubt that certain of the savants have taken great offense.” He defended his heliocentric thesis by stating “For it is the job of the astronomer to use painstaking and skilled observations in gathering together the history of the celestial movements‐ and then – since he cannot by any line of reasoning reach the true causes of these movements‐ to think up or construct whatever causes or hypotheses he pleases such that, by the assumption of these causes, those same movements can be calculated from the principles of geometry for the past and the future too. This artist is markedly outstanding in both of these respects: for it is not necessary that these hypotheses should be true, or even probable; but it is enough if they provide a calculus which fits the observations.” This preface written in 1543 sounds in its arguments very modern physics. However, historians of science have discovered that it was probably written by a theologian friend of Copernicus to defend the book against the criticism by the church.

Bacon's intermediate hypotheses

In his book Novum Organum , Francis Bacon ( 1561–1626 ) claims for hypotheses and scientific reasoning “that they augur well for the sciences, when the ascent shall proceed by a true scale and successive steps, without interruption or breach, from particulars to the lesser axioms, thence to the intermediates and lastly to the most general.” He then notes “that the lowest axioms differ but little from bare experiments, the highest and most general are notional, abstract, and of no real weight. The intermediate are true, solid, full of life, and up to them depend the business and fortune of mankind.” He warns that “we must not then add wings, but rather lead and ballast to the understanding, to prevent its jumping and flying, which has not yet been done; but whenever this takes place we may entertain greater hopes of the sciences.” With respect to methodology, Bacon claims that “we must invent a different form of induction. The induction which proceeds by simple enumeration is puerile, leads to uncertain conclusions, …deciding generally from too small a number of facts. Sciences should separate nature by proper rejections and exclusions and then conclude for the affirmative, after collecting a sufficient number of negatives.”

Gilbert and Descartes for plausible hypotheses

William Gilbert introduced in his book On the Loadstone (Gilbert,  1544‐1603 ) the argument of plausibility into physical hypothesis building. “From these arguments, therefore, we infer not with mere probability, but with certainty, the diurnal rotation of the earth; for nature ever acts with fewer than with many means; and because it is more accordant to reason that the one small body, the earth, should make a daily revolution than the whole universe should be whirled around it.”

Descartes ( 1596‐1650 ) reflected on the sources of understanding in his book Rules for Direction and distinguished what “comes about by impulse, by conjecture, or by deduction. Impulse can assign no reason for their belief and when determined by fanciful disposition, it is almost always a source of error.” When speaking about the working of conjectures he quotes thoughts of Aristotle: “water which is at a greater distance from the center of the globe than earth is likewise less dense substance, and likewise the air which is above the water, is still rarer. Hence, we hazard the guess that above the air nothing exists but a very pure ether which is much rarer than air itself. Moreover nothing that we construct in this way really deceives, if we merely judge it to be probable and never affirm it to be true; in fact it makes us better instructed. Deduction is thus left to us as the only means of putting things together so as to be sure of their truth. Yet in it, too, there may be many defects.”

Care in formulating hypotheses

Locke ( 1632‐1704 ) in his treatise Concerning Human Understanding admits that “we may make use of any probable hypotheses whatsoever. Hypotheses if they are well made are at least great helps to the memory and often direct us to new discoveries. However, we should not take up any one too hastily.” Also, practising scientists argued against careless use of hypotheses and proposed remedies. Lavoisier ( 1743‐1794 ) in the preface to his Element of Chemistry warned about beaten‐track hypotheses. “Instead of applying observation to the things we wished to know, we have chosen rather to imagine them. Advancing from one ill‐founded supposition to another, we have at last bewildered ourselves amidst a multitude of errors. These errors becoming prejudices, are adopted as principles and we thus bewilder ourselves more and more. We abuse words which we do not understand. There is but one remedy: this is to forget all that we have learned, to trace back our ideas to their sources and as Bacon says to frame the human understanding anew.”

Faraday ( 1791–1867 ) in a Speculation Touching Electric Conduction and the Nature of Matter highlighted the fundamental difference between hypotheses and facts when noting “that he has most power of penetrating the secrets of nature, and guessing by hypothesis at her mode of working, will also be most careful for his own safe progress and that of others, to distinguish that knowledge which consists of assumption, by which I mean theory and hypothesis, from that which is the knowledge of facts and laws; never raising the former to the dignity or authority of the latter.”

Explicatory power justifies hypotheses

Darwin ( 1809 –1882a) defended the conclusions and hypothesis of his book The Origin of Species “that species have been modified in a long course of descent. This has been affected chiefly through the natural selection of numerous, slight, favorable variations.” He uses a post hoc argument for this hypothesis: “It can hardly be supposed that a false theory would explain, to so satisfactory a manner as does the theory of natural selection, the several large classes of facts” described in his book.

The natural selection of hypotheses

In the concluding chapter of The Descent of Man Darwin ( 1809 –1882b) admits “that many of the views which have been advanced in this book are highly speculative and some no doubt will prove erroneous.” However, he distinguished that “false facts are highly injurious to the progress of science for they often endure long; but false views do little harm for everyone takes a salutory pleasure in proving their falseness; and when this is done, one path to error is closed and the road to truth is often at the same time opened.”

The American philosopher William James ( 1842–1907 ) concurred with Darwin's view when he wrote in his Principles of Psychology “every scientific conception is in the first instance a spontaneous variation in someone'’s brain. For one that proves useful and applicable there are a thousand that perish through their worthlessness. The scientific conceptions must prove their worth by being verified. This test, however, is the cause of their preservation, not of their production.”

The American philosopher J. Dewey ( 1859‐1952 ) in his treatise Experience and Education notes that “the experimental method of science attaches more importance not less to ideas than do other methods. There is no such thing as experiment in the scientific sense unless action is directed by some leading idea. The fact that the ideas employed are hypotheses, not final truths, is the reason why ideas are more jealously guarded and tested in science than anywhere else. As fixed truths they must be accepted and that is the end of the matter. But as hypotheses, they must be continuously tested and revised, a requirement that demands they be accurately formulated. Ideas or hypotheses are tested by the consequences which they produce when they are acted upon. The method of intelligence manifested in the experimental method demands keeping track of ideas, activities, and observed consequences. Keeping track is a matter of reflective review.”

The reductionist principle

James ( 1842‐1907 ) pushed this idea further when saying “Scientific thought goes by selection. We break the solid plenitude of fact into separate essences, conceive generally what only exists particularly, and by our classifications leave nothing in its natural neighborhood. The reality exists as a plenum. All its part are contemporaneous, but we can neither experience nor think this plenum. What we experience is a chaos of fragmentary impressions, what we think is an abstract system of hypothetical data and laws. We must decompose each chaos into single facts. We must learn to see in the chaotic antecedent a multitude of distinct antecedents, in the chaotic consequent a multitude of distinct consequents.” From these considerations James concluded “even those experiences which are used to prove a scientific truth are for the most part artificial experiences of the laboratory gained after the truth itself has been conjectured. Instead of experiences engendering the inner relations, the inner relations are what engender the experience here.“

Following curiosity

Freud ( 1856–1939 ) considered curiosity and imagination as driving forces of hypothesis building which need to be confronted as quickly as possible with observations. In Beyond the Pleasure Principle , Freud wrote “One may surely give oneself up to a line of thought and follow it up as far as it leads, simply out of scientific curiosity. These innovations were direct translations of observation into theory, subject to no greater sources of error than is inevitable in anything of the kind. At all events there is no way of working out this idea except by combining facts with pure imagination and thereby departing far from observation.” This can quickly go astray when trusting intuition. Freud recommends “that one may inexorably reject theories that are contradicted by the very first steps in the analysis of observation and be aware that those one holds have only a tentative validity.”

Feed‐forward aspects of hypotheses

The geneticist Waddington ( 1905–1975 ) in his essay The Nature of Life states that “a scientific theory cannot remain a mere structure within the world of logic, but must have implications for action and that in two rather different ways. It must involve the consequence that if you do so and so, such and such result will follow. That is to say it must give, or at least offer, the possibility of controlling the process. Secondly, its value is quite largely dependent on its power of suggesting the next step in scientific advance. Any complete piece of scientific work starts with an activity essentially the same as that of an artist. It starts by asking a relevant question. The first step may be a new awareness of some facet of the world that no one else had previously thought worth attending to. Or some new imaginative idea which depends on a sensitive receptiveness to the oddity of nature essentially similar to that of the artist. In his logical analysis and manipulative experimentation, the scientist is behaving arrogantly towards nature, trying to force her into his categories of thought or to trick her into doing what he wants. But finally he has to be humble. He has to take his intuition, his logical theory and his manipulative skill to the bar of Nature and see whether she answers yes or no; and he has to abide by the result. Science is often quite ready to tolerate some logical inadequacy in a theory‐or even a flat logical contradiction like that between the particle and wave theories of matter‐so long as it finds itself in the possession of a hypothesis which offers both the possibility of control and a guide to worthwhile avenues of exploration.”

Poincaré: the dialogue between experiment and hypothesis

Poincaré ( 1854 –1912b) also dealt with physics in Science and Hypothesis . “Experiment is the sole source of truth. It alone can teach us certainty. Cannot we be content with experiment alone? What place is left for mathematical physics? The man of science must work with method. Science is built up of facts, as a house is built of stones, but an accumulation of facts is no more a science than a heap of stones is a house. It is often said that experiments should be made without preconceived concepts. That is impossible. Without the hypothesis, no conclusion could have been drawn; nothing extraordinary would have been seen; and only one fact the more would have been catalogued, without deducing from it the remotest consequence.” Poincaré compares science to a library. Experimental physics alone can enrich the library with new books, but mathematical theoretical physics draw up the catalogue to find the books and to reveal gaps which have to be closed by the purchase of new books.

Poincaré: false, true, fruitful and dangerous hypotheses

Poincaré continues “we all know that there are good and bad experiments. The latter accumulate in vain. Whether there are hundred or thousand, one single piece of work will be sufficient to sweep them into oblivion. Bacon invented the term of an experimentum crucis for such experiments. What then is a good experiment? It is that which teaches us something more than an isolated fact. It is that which enables us to predict and to generalize. Experiments only gives us a certain number of isolated points. They must be connected by a continuous line and that is true generalization. Every generalization is a hypothesis. It should be as soon as possible submitted to verification. If it cannot stand the test, it must be abandoned without any hesitation. The physicist who has just given up one of his hypotheses should rejoice, for he found an unexpected opportunity of discovery. The hypothesis took into account all the known factors which seem capable of intervention in the phenomenon. If it is not verified, it is because there is something unexpected. Has the hypothesis thus rejected been sterile? Far from it. It has rendered more service than a true hypothesis.” Poincaré notes that “with a true hypothesis only one fact the more would have been catalogued, without deducing from it the remotest consequence. It may be said that the wrong hypothesis has rendered more service than a true hypothesis.” However, Poincaré warns that “some hypotheses are dangerous – first and foremost those which are tacit and unconscious. And since we make them without knowing them, we cannot get rid of them.” Poincaré notes that here mathematical physics is of help because by its precision one is compelled to formulate all the hypotheses, revealing also the tacit ones.

Arguments for the reductionist principle

Poincaré also warned against multiplying hypotheses indefinitely: “If we construct a theory upon multiple hypotheses, and if experiment condemns it, which of the premisses must be changed?” Poincaré also recommended to “resolve the complex phenomenon given directly by experiment into a very large number of elementary phenomena. First, with respect to time. Instead of embracing in its entirety the progressive development of a phenomenon, we simply try to connect each moment with the one immediately preceding. Next, we try to decompose the phenomenon in space. We must try to deduce the elementary phenomenon localized in a very small region of space.” Poincaré suggested that the physicist should “be guided by the instinct of simplicity, and that is why in physical science generalization so readily takes the mathematical form to state the problem in the form of an equation.” This argument goes back to Galilei ( 1564 –1642b) who wrote in The Two Sciences “when I observe a stone initially at rest falling from an elevated position and continually acquiring new increments of speed, why should I not believe that such increases take place in a manner which is exceedingly simple and rather obvious to everybody? If now we examine the matter carefully we find no addition or increment more simple than that which repeats itself always in the same manner. It seems we shall not be far wrong if we put the increment of speed as proportional to the increment of time.” With a bit of geometrical reasoning, Galilei deduced that the distance travelled by a freely falling body varies as the square of the time. However, Galilei was not naïve and continued “I grant that these conclusions proved in the abstract will be different when applied in the concrete” and considers disturbances cause by friction and air resistance that complicate the initially conceived simplicity.

Four sequential steps of discovery…

Some philosophers of science attributed a fundamental importance to observations for the acquisition of experience in science. The process starts with accidental observations (Aristotle), going to systematic observations (Bacon), leading to quantitative rules obtained with exact measurements (Newton and Kant) and culminating in observations under artificially created conditions in experiments (Galilei) (Mittelstrass,  1980g ).

…rejected by Popper and Kant

In fact, Newton wrote that he had developed his theory of gravitation from experience followed by induction. K. Popper ( 1902‐1994 ) in his book Conjectures and Refutations did not agree with this logical flow “experience leading to theory” and that for several reasons. This scheme is according to Popper intuitively false because observations are always inexact, while theory makes absolute exact assertions. It is also historically false because Copernicus and Kepler were not led to their theories by experimental observations but by geometry and number theories of Plato and Pythagoras for which they searched verifications in observational data. Kepler, for example, tried to prove the concept of circular planetary movement influenced by Greek theory of the circle being a perfect geometric figure and only when he could not demonstrate this with observational data, he tried elliptical movements. Popper noted that it was Kant who realized that even physical experiments are not prior to theories when quoting Kant's preface to the Critique of Pure Reason : “When Galilei let his globes run down an inclined plane with a gravity which he has chosen himself, then a light dawned on all natural philosophers. They learnt that our reason can only understand what it creates according to its own design; that we must compel Nature to answer our questions, rather than cling to Nature's apron strings and allow her to guide us. For purely accidental observations, made without any plan having been thought out in advance, cannot be connected by a law‐ which is what reason is searching for.” From that reasoning Popper concluded that “we ourselves must confront nature with hypotheses and demand a reply to our questions; and that lacking such hypotheses, we can only make haphazard observations which follow no plan and which can therefore never lead to a natural law. Everyday experience, too, goes far beyond all observations. Everyday experience must interpret observations for without theoretical interpretation, observations remain blind and uninformative. Everyday experience constantly operates with abstract ideas, such as that of cause and effect, and so it cannot be derived from observation.” Popper agreed with Kant who said “Our intellect does not draw its laws from nature…but imposes them on nature”. Popper modifies this statement to “Our intellect does not draw its laws from nature, but tries‐ with varying degrees of success – to impose upon nature laws which it freely invents. Theories are seen to be free creations of our mind, the result of almost poetic intuition. While theories cannot be logically derived from observations, they can, however, clash with observations. This fact makes it possible to infer from observations that a theory is false. The possibility of refuting theories by observations is the basis of all empirical tests. All empirical tests are therefore attempted refutations.”

OUTLOOK: HYPOTHESES IN BIOLOGY

Is biology special.

Waddington notes that “living organisms are much more complicated than the non‐living things. Biology has therefore developed more slowly than sciences such as physics and chemistry and has tended to rely on them for many of its basic ideas. These older physical sciences have provided biology with many firm foundations which have been of the greatest value to it, but throughout most of its history biology has found itself faced with the dilemma as to how far its reliance on physics and chemistry should be pushed” both with respect to its experimental methods and its theoretical foundations. Vitalism is indeed such a theory maintaining that organisms cannot be explained solely by physicochemical laws claiming specific biological forces active in organisms. However, efforts to prove the existence of such vital forces have failed and today most biologists consider vitalism a superseded theory.

Biology as a branch of science is as old as physics. If one takes Aristotle as a reference, he has written more on biology than on physics. Sophisticated animal experiments were already conducted in the antiquity by Galen (Brüssow, 2022 ). Alertus Magnus displayed biological research interest during the medieval time. Knowledge on plants provided the basis of medical drugs in early modern times. What explains biology's decreasing influence compared with the rapid development of physics by Galilei and Newton? One reason is the possibility to use mathematical equations to describe physical phenomena which was not possible for biological phenomena. Physics has from the beginning displayed a trend to few fundamental underlying principles. This is not the case for biology. With the discovery of new continents, biologists were fascinated by the diversity of life. Diversity was the conducting line of biological thinking. This changed only when taxonomists and comparative anatomists revealed recurring pattern in this stunning biological variety and when Darwin provided a theoretical concept to understand variation as a driving force in biology. Even when genetics and molecular biology allowed to understand biology from a few universally shared properties, such as a universal genetic code, biology differed in fundamental aspects from physics and chemistry. First, biology is so far restricted to the planet earth while the laws of physic and chemistry apply in principle to the entire universe. Second, biology is to a great extent a historical discipline; many biological processes cannot be understood from present‐day observations because they are the result of historical developments in evolution. Hence, the importance of Dobzhansky's dictum that nothing makes sense in biology except in the light of evolution. The great diversity of life forms, the complexity of processes occurring in cells and their integration in higher organisms and the importance of a historical past for the understanding of extant organisms, all that has delayed the successful application of mathematical methods in biology or the construction of theoretical frameworks in biology. Theoretical biology by far did not achieve a comparable role as theoretical physics which is on equal foot with experimental physics. Many biologists are even rather sceptical towards a theoretical biology and see progress in the development of ever more sophisticated experimental methods instead in theoretical concepts expressed by new hypotheses.

Knowledge from data without hypothesis?

Philosophers distinguish rational knowledge ( cognitio ex principiis ) from knowledge from data ( cognitio ex data ). Kant associates these two branches with natural sciences and natural history, respectively. The latter with descriptions of natural objects as prominently done with systematic classification of animals and plants or, where it is really history, when describing events in the evolution of life forms on earth. Cognitio ex data thus played a much more prominent role in biology than in physics and explains why the compilation of data and in extremis the collection of museum specimen characterizes biological research. To account for this difference, philosophers of the logical empiricism developed a two‐level concept of science languages consisting of a language of observations (Beobachtungssprache) and a language of theories (Theoriesprache) which are linked by certain rules of correspondence (Korrespondenzregeln) (Carnap,  1891 –1970d). If one looks into leading biological research journals, it becomes clear that biology has a sophisticated language of observation and a much less developed language of theories.

Do we need more philosophical thinking in biology or at least a more vigorous theoretical biology? The breathtaking speed of progress in experimental biology seems to indicate that biology can well develop without much theoretical or philosophical thinking. At the same time, one could argue that some fields in biology might need more theoretical rigour. Microbiologists might think on microbiome research—one of the breakthrough developments of microbiology research in recent years. The field teems with fascinating, but ill‐defined terms (our second genome; holobionts; gut–brain axis; dysbiosis, symbionts; probiotics; health benefits) that call for stricter definitions. One might also argue that biologists should at least consider the criticism of Goethe ( 1749–1832 ), a poet who was also an active scientist. In Faust , the devil ironically teaches biology to a young student.

“Wer will was Lebendigs erkennen und beschreiben, Sucht erst den Geist herauszutreiben, Dann hat er die Teile in seiner Hand, Fehlt, leider! nur das geistige Band.” (To docket living things past any doubt. You cancel first the living spirit out: The parts lie in the hollow of your hand, You only lack the living thing you banned).

We probably need both in biology: more data and more theory and hypotheses.

CONFLICT OF INTEREST

The author reports no conflict of interest.

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Supporting information

Appendix S1

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