function of hypothesis to research

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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What Is 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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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

The Research Hypothesis: Role and Construction

• First Online: 01 January 2012

Cite this chapter

• Phyllis G. Supino EdD 3  

6017 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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De Morgan A, De Morgan S. A budget of paradoxes. London: Longmans Green; 1872.

Google Scholar  

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

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

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

Article   PubMed   Google Scholar  

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

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

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

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

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

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

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

Article   PubMed   CAS   Google Scholar  

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

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

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

Article   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

PubMed   CAS   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What is a Research Hypothesis?

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

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

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

Importance of Hypothesis in Research

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

• A research hypothesis helps test theories.

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

• It serves as a great platform for investigation activities.

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

• Hypothesis guides the research work or study.

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

• Hypothesis sometimes suggests theories.

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

• It helps in knowing the data needs.

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

• The hypothesis explains social phenomena.

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

• Hypothesis provides a relationship between phenomena for empirical Testing.

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

• It helps in knowing the most suitable analysis technique.

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

Characteristics of a Good Research Hypothesis

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

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

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

Types of Research Hypotheses

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

01. Null Hypothesis

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

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

02. Alternative Hypothesis

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

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

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

03. Directional Hypothesis

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

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

04. Non-directional Hypothesis

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

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

05. Simple Hypothesis

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

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

06. Complex Hypothesis

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

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

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

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

07. Associative Hypothesis

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

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

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

08. Causal Hypothesis

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

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

09. Empirical Hypothesis

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

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

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

10. Statistical Hypothesis

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

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

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

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

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

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

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

Step 2: Conduct a literature review

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

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

Step 3: Formulate your research question

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

Step 4: Identify variables

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

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

Step 5: State the Null hypothesis

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

Step 6: Select appropriate methods for testing the hypothesis

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

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

Testing and Evaluating Hypotheses

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

• State your research hypothesis.

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

• Collect data strategically.

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

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

• Perform an appropriate statistical test.

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

• Decide if your idea was right or wrong.

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

• Share what you found.

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

The Role of QuestionPro to Develop a Good Research Hypothesis

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

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

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

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

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

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Hypothesis: Functions, Problems, Types, Characteristics, Examples

Basic Elements of the Scientific Method: Hypotheses

The Function of the Hypotheses

A hypothesis states what one is looking for in an experiment. When facts are assembled, ordered, and seen in a relationship, they build up to become a theory. This theory needs to be deduced for further confirmation of the facts, this formulation of the deductions constitutes of a hypothesis. As a theory states a logical relationship between facts and from this, the propositions which are deduced should be true. Hence, these deduced prepositions are called hypotheses.

Problems in Formulating the Hypothesis

As difficult as the process may be, it is very essential to understand the need of a hypothesis. The research would be much unfocused and a random empirical wandering without it. The hypothesis provides a necessary link between the theory and investigation which often leads to the discovery of additions to knowledge.

There are three major difficulties in the formulation of a hypothesis, they are as follows:

• Absence of a clear theoretical framework
• Lack of ability to utilize that theoretical framework logically
• Failure to be acquainted with available research techniques so as to phrase the hypothesis properly.

Sometimes the deduction of a hypothesis may be difficult as there would be many variables and the necessity to take them all into consideration becomes a challenge. For instance, observing two cases:

• Principle: A socially recognized relationship with built-in strains also governed by the institutional controls has to ensure conformity of the participants with implicit or explicit norms.

Deduction: This situation holds much more sense to the people who are in professions such as psychotherapy, psychiatry and law to some extent. They possess a very intimate relationship with their clients, thus are more susceptible to issues regarding emotional strains in the client-practitioner relationship and more implicit and explicit controls over both participants in comparison to other professions.

The above-mentioned case has variable hypotheses, so the need is to break them down into sub hypotheses, they are as follows:

• Specification of the degree of difference
• Specification of profession and problem
• Specification of kinds of controls.

2. Principle: Extensive but relatively systematized data show the correlation between members of the upper occupational class and less unhappiness and worry. Also, they are subjected to more formal controls than members of the lower strata.

Deduction: There can numerous ways to approach this principle, one could go with the comparison applying to martial relationships of the members and further argue that such differential pressures could be observed through divorce rates. This hypothesis would show inverse correlations between class position and divorce rates. There would be a very strong need to define the terms carefully to show the deduction from the principle problem.

The reference of these examples showcases a major issue in the hypothesis formulations procedures. One needs to keep the lines set for the deductions and one should be focusing on having a hypothesis at the beginning of the experiment, that hypothesis may be subject to change in the later stages and it is referred to as a „working hypothesis. Hence, the devising and utilization of a hypothesis is essential for the success of the experiment.

Types of Hypothesis

There are many ways to classify hypotheses, but it seems adequate to distinguish to separate them on the basis of their level of abstraction. They can be divided into three broad levels which will be increasing in abstractness.

• The existence of empirical uniformities : These hypotheses are made from problems which usually have a very high percentage of representing scientific examination of common–sense proportions. These studies may show a variety of things such as the distribution of business establishments in a city, behavior patterns of specific groups, etc. and they tend to show no irregularities in their data collection or review. There have been arguments which say that these aren’t hypothesis as they represent what everyone knows. This can be counter argued on the basis of two things that, “what everyone knows” isn’t always in coherence with the framework of science and it may also be incorrect. Hence, testing these hypotheses is necessary too.
• Complex ideal types: These hypotheses aim at testing the existence of logically derived relationships between empirical uniformities. This can be understood with an example, to observe ecology one should take in many factors and see the relationship between and how they affect the greater issue. A theory by Ernest W. Burgess gave out the statement that concentric growth circles are the one which characterize the city. Hence, all issues such as land values, industrial growth, ethnic groups, etc. are needed to be analyzed for forming a correct and reasonable hypothesis.
• Relations of analytic variables: These hypotheses are a bit more complex as they focus on they lead to the formulation of a relationship between the changes in one property with respect to another. For instance, taking the example of human fertility in diverse regions, religions, wealth gap, etc. may not always affect the end result but it doesn’t mean that the variables need not be accounted for. This level of hypothesizing is one of the most effective and sophisticated and thus is only limited by theory itself.

Science and Hypothesis

“The general culture in which a science develops furnishes many of its basic hypotheses” holds true as science has developed more in the West and is no accident that it is a function of culture itself. This is quite evident with the culture of the West as they read for morals, science and happiness. After the examination of a bunch of variables, it is quite easy to say that the cultural emphasis upon happiness has been productive of an almost limitless range.

The hypotheses originate from science; a key example in the form of “socialization” may be taken. The socialization process in learning science involves a feedback mechanism between the scientist and the student. The student learns from the scientist and then tests for results with his own experience, and the scientist in turn has to do the same with his colleagues.

Analogies are a source of useful hypotheses but not without its dangers as all variables may not be accounted for it as no civilization has a perfect system.

Hypotheses are also the consequence of personal, idiosyncratic experience as the manner in which the individual reacts to the hypotheses is also important and should be accounted for in the experiment.

The Characteristics for Usable Hypotheses

The criteria for judging a hypothesis as mentioned below:

• Complete Clarity : A good hypothesis should have two main elements, the concepts should be clearly defined and they should be definitions which are communicable and accepted by a larger section of the public. A lot of sources may be used and fellow associates may be used to help with the cause.
• Empirical Referents : A great hypothesis should have scientific concepts with the ultimate empirical referent. It can‟t be based on moral judgment though it can explore them but the goal should be separated from moral preachment and the acceptance of values. A good start could be analyzing the concepts which express attitudes rather than describing or referring to empirical phenomena.
• Specific Goal : The goal and procedure of the hypothesis should be tangible as grand experiments are harder to carry out. All operations and predictions should be mapped and in turn the possibility of testing the hypothesis increases. This not only enables the conceptual clarity but also the description of any indexes used. These indexes are used as variables for testing hypotheses on a larger scale. A general prediction isn’t as reliable as a specific prediction as the specific prediction provides a better result.
• Relation to Available Techniques : The technique with which a hypothesis is tested is of the utmost importance and so thorough research should be carried out before the experiment in order to find the best possible way to go about it. The example of Karl Marx may be given regarding his renowned theories; he formulated his hypothesis by observing individuals and thus proving his hypothesis. So, finding the right technique may be the key to a successful test.
• Relation to a Body of Theory: Theories on social relations can never be developed in isolation but they are a further extension of already developed or developing theories. For instance, if the “intelligence quotient” of a member of the society is to be measured, certain variables such as caste, ethnicity, nationality, etc. are chosen thus deductions are made from time to time to eventually find out what is the factor that influences intelligence.

The Conclusion

The formulation of a hypothesis is probably the most necessary step in good research practice and it is very essential to get the thought process started. It helps the researcher to have a specific goal in mind and deduce the end result of an experiment with ease and efficiency. History is evident that asking the right questions always works out fine.

Also Read: Research Methods – Basics

Goode, W. E. and P. K. Hatt. 1952. Methods in Social Research.New York: McGraw Hill. Chapters 5 and 6. Pp. 41-73

Kartik is studying BA in International Relations at Amity and Dropped out of engineering from NIT Hamirpur and he lived in over 5 different countries.

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

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

Mohit Bhandari

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

Objectives of this article

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

Research question

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

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

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

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

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

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

PICOT criteria 1

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

Research hypothesis

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

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

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

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

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

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

Research objective

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

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

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

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

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

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

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

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

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

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

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

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

Public Health Notes

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 Hypothesis? Definition, Meaning, Characteristics, Sources

• Post last modified: 10 January 2022
• Reading time: 18 mins read
• Post category: Research Methodology

• What is Hypothesis?

Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.

As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.

In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.

Table of Content

• 1 What is Hypothesis?
• 2 Hypothesis Definition
• 3 Meaning of Hypothesis
• 4.1 Conceptual Clarity
• 4.2 Need of empirical referents
• 4.3 Hypothesis should be specific
• 4.4 Hypothesis should be within the ambit of the available research techniques
• 4.5 Hypothesis should be consistent with the theory
• 4.6 Hypothesis should be concerned with observable facts and empirical events
• 4.7 Hypothesis should be simple
• 5.1 Observation
• 5.2 Analogies
• 5.4 State of Knowledge
• 5.5 Culture
• 5.6 Continuity of Research
• 6.1 Null Hypothesis
• 6.2 Alternative Hypothesis

Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.

The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).

Hypothesis Definition

A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart

Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black

Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt

A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)

Meaning of Hypothesis

From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.

• At the primary level, a hypothesis is the possible and probable explanation of the sequence of happenings or data.
• Sometimes, hypothesis may emerge from an imagination, common sense or a sudden event.
• Hypothesis can be a probable answer to the research problem undertaken for study. 4. Hypothesis may not always be true. It can get disproven. In other words, hypothesis need not always be a true proposition.
• Hypothesis, in a sense, is an attempt to present the interrelations that exist in the available data or information.
• Hypothesis is not an individual opinion or community thought. Instead, it is a philosophical means which is to be used for research purpose. Hypothesis is not to be considered as the ultimate objective; rather it is to be taken as the means of explaining scientifically the prevailing situation.

The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.

Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.

Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.

• Population growth moderates with the rise in per capita income.
• Sales growth is positively linked with the availability of credit.
• Commerce education increases the employability of the graduate students.
• High rates of direct taxes prompt people to evade taxes.
• Good working conditions improve the productivity of employees.
• Advertising is the most effecting way of promoting sales than any other scheme.
• Higher Debt-Equity Ratio increases the probability of insolvency.
• Economic reforms in India have made the public sector banks more efficient and competent.
• Foreign direct investment in India has moved in those sectors which offer higher rate of profit.
• There is no significant association between credit rating and investment of fund.

Characteristics of Hypothesis

Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:

Conceptual Clarity

Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.

The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.

A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.

A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.

For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.

For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.

For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.

While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.

It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.

A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.

If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.

This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”

If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.

Sources of Hypothesis

Hypotheses can be derived from various sources. Some of the sources is given below:

Observation

State of knowledge, continuity of research.

Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.

Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.

This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.

An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.

Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.

The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.

Null and Alternative Hypothesis

Null hypothesis.

The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.

Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.

Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .

Alternative Hypothesis

Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.

As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .

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What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

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

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

What is Hypothesis?

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Sources of Hypothesis

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.

Non-directional Hypothesis Example

• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

How Hypothesis help in Scientific Research?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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Abdelghani Maddi

May 16th, 2024, the best peer review reports are at least 947 words.

0 comments | 17 shares

Estimated reading time: 6 minutes

Based on an analysis of the relationship between peer review reports and subsequent citations, Abdelghani Maddi argues that longer and hence more constructive and engaged peer review reports are closely associated with papers that are more cited.

Peer review continues to be the focus of considerable debate across academic fields as diverse as the sociology of science, economics and biology. This focus reflects a divide within the scientific community regarding its efficacy and role in identifying errors, and instances of scientific misconduct. In recent years it has been fueled by a proliferation of notable cases of errors and misconduct detected in articles published in prestigious international journals, including those published by Elsevier and Springer Nature. The rise of generative AI has again only heightened concerns due to its overt and covert use in peer review .

Nonetheless, there exists a consensus within the scientific community regarding the pivotal role of peer review in knowledge generation and dissemination. Despite uncertainties surrounding its ability to detect fraud and questionable practices, reviewers’ comments typically aim to provide constructive feedback aimed at enhancing the quality of papers, encompassing both structural and substantive aspects, including methodological validation and alignment to recognised publication standards.

However, as we found in a recent paper , quantifying the added value and tangible impact conferred by peer review on scientific publications remains challenging. The approach we opted for was to focus on the length of peer review reports. Our theory being that there is a direct correlation between the length of reports and the extent of revisions and modifications requested from authors. Hence, longer reports are, generally, associated with a higher likelihood of authors making revisions and enhancements to their papers, thereby bolstering their quality, visibility, and citation metrics.

We used data from Publons to extract information regarding the length of reviewers’ reports for a corpus of 57,482 publications. To ensure generalisability, the structure of the Publons database was adjusted with that of the Web of Science database using the Raking Ratio method, employing a control group comprising 12.3 million articles. Consequently, the weighted sample faithfully mirrors the overall database structure across disciplinary distribution, collaborative patterns, open access practices, among other variables.

papers garnering the highest citation counts tended to be associated with longer reviewer reports, exceeding the average length.

Our analyses revealed a statistically significant impact of reviewers’ report length on citations received, with reports surpassing approximately one and a half pages (947 words) marking a critical threshold. Notably, papers garnering the highest citation counts tended to be associated with longer reviewer reports, exceeding the average length. Beyond this threshold, citation counts exhibited an increasing trend with longer report lengths, corroborating the initial hypothesis positing the synonymous relationship between the length of referees’ reports and the extent of revisions solicited, thereby enhancing manuscript “quality”.

This finding highlights the role of reviewers in enhancing the quality of scientific publications. Their contribution extends far beyond mere minor revisions including spelling or grammatical corrections. Reviewers, who can be aptly termed the “unsung heroes” of scientific research, significantly contribute to improving publication quality by providing detailed and constructive reports, even within shrinking deadlines. Yet, their contribution remains largely unrecognised and underestimated.

Another salient aspect raised by the study is the importance of time for conducting thorough peer review. Journal editors need to acknowledge that furnishing useful, comprehensive reports entails a considerable investment of time and effort from reviewers. Soliciting evaluations within extremely short time frames, such as a week for reading the publication and drafting the report, as practiced by certain “gray” publishers, can compromise the quality of assessments and consequently diminish the value added by peer review to manuscripts. This points to the need to reconsider the emphasis on speed in response times for peer review.

Journal editors need to acknowledge that furnishing useful, comprehensive reports entails a considerable investment of time and effort from reviewers.

A third implication is related to the current saturation of the scientific publishing system , where the increasing number of submitted articles strains journals, reviewers, and the scientific community as a whole. This workload overload risks compromising the focus of reviewers, who must assess a large number of articles within short deadlines. This could lead to a risk of disseminating “bad science” if certain important aspects of an articles are not properly evaluated due to time constraints. Especially so in a context where the speed of peer review has become a sort of “advertising” argument wielded by some publishers to attract authors. Even though post-publication peer review allows for partial “correction” of errors and questionable practices, the volume of annual publications far exceeds the capacities of the active community to scrutinise all publications. The scientific community requires a pre-publication evaluation system that functions properly.

Finally, open peer review offers promising prospects for enhancing transparency and vigilance. According to one recent study the peer review model adopted by a journal has a direct influence on the behaviour of both researchers and reviewers. The study highlights that journals implementing open peer review protocols incentivise heightened engagement from researchers and reviewers, as their actions directly impact their reputations. In addition, by allowing access to reviewer reports and promoting the reuse of evaluation data, this approach could facilitate deeper and more quantitative analyses of the impact of peer review on the quality of scientific publications and the advancement of science in general. Thus, open peer review represents an opportunity to rethink and improve peer review practices in a context where the increasing quantity of publications necessitates a more efficient and transparent approach.

This post draws on the authors article, On the peer review reports: does size matter? , published in Scientometrics.

The content generated on this blog is for information purposes only. This Article gives the views and opinions of the authors and does not reflect the views and opinions of the Impact of Social Science blog (the blog), nor of the London School of Economics and Political Science. Please review our  comments policy  if you have any concerns on posting a comment below.

Image Credit:  nampix  on Shutterstock . 

About the author

Abdelghani Maddi is a research engineer at GEMASS (CNRS/Sorbonne University). Economist by training specializing in Scientometrics. He is passionate about understanding scientific knowledge production, promoting open science, and improving research evaluation.

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• Published: 13 May 2024

Brain clearance is reduced during sleep and anesthesia

• Andawei Miao   ORCID: orcid.org/0000-0002-0479-6945 1 , 2   na1 ,
• Tianyuan Luo 1   na1   nAff5   nAff6 ,
• Bryan Hsieh 1 , 3 ,
• Christopher J. Edge 1 ,
• Morgan Gridley   ORCID: orcid.org/0009-0007-2032-3666 1 ,
• Ryan Tak Chun Wong   ORCID: orcid.org/0009-0001-6131-6549 1 ,
• Timothy G. Constandinou   ORCID: orcid.org/0000-0001-9778-1162 4 ,
• William Wisden   ORCID: orcid.org/0000-0003-4743-0334 1 , 2 , 3 &
• Nicholas P. Franks   ORCID: orcid.org/0000-0003-4874-4212 1 , 2 , 3  

Nature Neuroscience ( 2024 ) Cite this article

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It has been suggested that the function of sleep is to actively clear metabolites and toxins from the brain. Enhanced clearance is also said to occur during anesthesia. Here, we measure clearance and movement of fluorescent molecules in the brains of male mice and show that movement is, in fact, independent of sleep and wake or anesthesia. Moreover, we show that brain clearance is markedly reduced, not increased, during sleep and anesthesia.

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Sleep pressure modulates single-neuron synapse number in zebrafish

Sleep is a state of vulnerable inactivity. Because of the risks that this vulnerability entails, most researchers assume that sleep must confer some essential benefit 1 , 2 , 3 . However, what this is remains a mystery. One suggestion is that sleep clears the brain of metabolites and toxins using the ‘glymphatic’ system, a process that cannot operate efficiently during the waking state 3 , 4 . This attractive idea has important implications. For example, diminished toxin clearance brought about by chronically poor sleep might exacerbate, if not cause, Alzheimer disease 5 , 6 .

How metabolites and toxins are cleared from the brain is unresolved. Disputes surround both the anatomical pathways 7 , 8 , 9 and the mechanisms of clearance 7 , 10 , 11 . The glymphatic hypothesis contends that bulk flow of fluid, rather than just diffusion, actively clears solutes from the brain parenchyma during non-rapid-eye-movement (NREM) sleep 3 . This flow is proposed to be driven by hydrostatic pressure gradients established by arterial pulsations 12 . Anesthetics at sedative doses, which induce states resembling deep NREM sleep 2 , 13 , were also reported to increase clearance 3 , 14 , 15 . However, whether sleep does enhance clearance by increased bulk flow is unresolved, with findings both supporting 3 , 4 , 12 , 14 , 15 , 16 and challenging 10 , 11 , 17 , 18 , 19 the idea. Here, we directly measure clearance and fluid movement in the brains of mice during different vigilance states (awake, sleeping or sedated).

We first determined the diffusion coefficient ( D ) of a fluorescent dye (fluorescein isothiocyanate, FITC-dextran) in brains of mice (Fig. 1a ). We injected 4 kDa FITC-dextran into the caudate putamen (CPu) and then monitored the fluorescence arriving in the frontal cortex. The first series of experiments involved waiting for steady state and then bleaching the dye in a small volume of tissue in the neocortex and determining D from the rate that unbleached dye moved into the bleached region, a technique pioneered by others 20 , 21 .

a , The experimental setup. Light from a 488-nm laser diode was passed through a 200-μm optical fiber into either an agarose gel brain phantom in vitro or the frontal cortex of a mouse in vivo. For the in vitro experiments, the agarose gel contained 4 kDa FITC-dextran while, for the in vivo experiments, the brain had been injected with 4 kDa FITC-dextran some hours earlier. b , A typical recording of photobleaching in an agarose gel brain phantom, fitted by least-squares to equation ( 5 ), to give (for this example) a value of D  = 136 μm 2  s −1 . The inset shows that the diffusion coefficient follows a power law, with D   ∝   M −0.44 . The red shading in the inset shows the s.e.m. c , A comparison between the diffusion coefficients determined directly (direct) ( Methods and Extended Data Fig. 3 ) and those determined using the photobleaching method (PB) was not significantly different (two-way ANOVA P  = 0.10). Top, the individual data points. Bottom, the differences in the diffusion coefficients determined using the two methods. The agreement between the methods was excellent at 4 kDa FITC-dextran and this was used for the in vivo measurements. d , Left, the diffusion coefficients of 4 kDa FITC-dextran as a function of the percentage of wake (state) during the hour the diffusion coefficient was being measured (the distribution of vigilance states is shown in the pie charts above). Each point represents the average of typically four measurements for an individual mouse and the number of mice, n , is shown above. The last group of data on the right-hand side were recorded during dexmedetomidine (DEX) sedation. Right, the mean differences relative to the average diffusion coefficient across all vigilance states. A one-way ANOVA gave F (4,55) = 0.90; P  = 0.47. (A difference of ~35% in D would have been detected.) e , Left, the diffusion coefficients as a function of zeitgeber time. Right, the mean differences relative to the average diffusion coefficient recorded over the circadian cycle. A one-way ANOVA gave F (5,64) = 0.88; P  = 0.50. In c – e , the vertical solid lines show the 95% confidence intervals; the shaded areas show the distributions of likelihood. In d and e , the horizontal solid and dashed lines show the s.e.m. and the mean, respectively.

Source data

We validated our methodology by measuring the diffusion of FITC–dextrans of various molecular weights in agarose ‘brain phantom’ gels, modified to approximate the light-scattering and optical-absorption properties of brain tissue 22 and found (Extended Data Fig. 1 ) that the distribution of light intensity was well approximated by a hemispherical Gaussian distribution. Immediately following 30 s of bleaching, we recorded the recovery of the fluorescence as unbleached dye moved into the bleached volume. Figure 1b shows a typical recording for 4 kDa FITC-dextran (blue trace). There was excellent agreement between these data and the time course predicted using equations ( 4 ) and ( 5 ) ( Methods and Extended Data Fig. 2 ).

Using this method, our measured diffusion coefficients were in good agreement with literature values in aqueous solutions 23 , 24 and their mass dependence (inset to Fig. 1b ). Our diffusion coefficients also agreed well (Fig. 1c ) with values obtained using a direct method (Extended Data Fig. 3 ) that did not involve photobleaching.

We then measured D in vivo using 4 kDa FITC-dextran, which after injection into the CPu, could be detected in the frontal cortex, where its fluorescence peaked at about 6–7 h postinjection, then slowly declined at ~6% per hour (Extended Data Fig. 4a ). During the slowly declining phase, approximating to steady state, the recovery from bleaching was recorded (and baseline corrected) ( Methods ). The spread of light in a brain using a brain slice ( Methods ) confirmed that the distribution was also well approximated by a hemispherical Gaussian distribution (Extended Data Fig. 4b ). As with the gel experiments described above, the fluorescence recovery agreed well with the theoretical predictions (Extended Data Fig. 4c ) and we derived values for the effective tissue D from the time courses, while also determining the vigilance states (Extended Data Fig. 4d ).

We observed no significant change in the diffusion coefficient of 4 kDa FITC-dextran with either vigilance state or dexmedetomidine (200 μg kg −1 ; intraperitoneal (i.p.) sedation (Fig. 1d ) or during the day–night cycle (Fig. 1e )). The mean value for D across all vigilance states was 32.1 ± 1.9 μm 2  s −1 ( n  = 52; mean ± s.e.m.), which corresponds, using equation ( 3 ), to a tortuosity of ~2.5 (having corrected the aqueous D to 37 °C using the Stokes–Einstein equation 25 ). This is consistent with values reported for rodent neocortex 25 and suggests that the movement of 4 kDa FITC-dextran in the cortex is predominantly by diffusion, a conclusion previously reached by others 11 , 18 , 19 . Notably, these results show that diffusion kinetics do not change during sleep or anesthesia. From separate in vitro measurements (Extended Data Fig. 5 ), we estimate that we could have detected a change in bulk flow between vigilance states of >0.5 μm s −1 but our results cannot rule out changes in pairwise flows in opposite directions over small distances in the surrounding tissue, which might have averaged out, so that brain clearance might, nonetheless, have changed. We therefore extended our experiments to measure brain clearance itself during different vigilance states.

The approach we took to measuring brain clearance used the same experimental setup as shown in Fig. 1a . However, it has recently been shown 16 that a small dye which moves freely in the parenchyma can be used to accurately quantify brain clearance (Fig. 2a ). This would also allow a complete time course to be recorded in the cortex as the dye spread throughout the brain. We used AF488 (~570 Da) and first showed that the spread in a gel, with no clearance possible, could be accounted for by equation ( 2 ), the spread from a Gaussian source. Figure 2b , shows that equation ( 2 ) fitted the experimental data essentially perfectly, with an aqueous diffusion coefficient of 295 μm 2  s −1 . In the absence of clearance and, if r (the distance between where dye is injected and where it is recorded) is constant, then the timing of the peak is determined only by the diffusion coefficient (Extended Data Fig. 6 ). If clearance occurs, the height of the peak would be reduced (Fig. 2c and equation ( 8 )).

a , A fluorescent dye (AF488) was injected into the CPu and the fluorescence monitored over time in the frontal cortex. b , The spread of the dye could be accurately predicted by equation ( 2 ) in an agarose gel with a diffusion coefficient of 295 μm 2  s −1 , where there was zero clearance. The error envelope represents the s.e.m. c , If brain clearance of the dye is assumed to increase with time as described by equation ( 9 ), then the concentration in the frontal cortex is predicted to follow the time course given by equation ( 8 ) and is shown by the dashed lines. Knowing the concentration that should have arrived at the cortex had there been no clearance (solid line), the percentage clearance can be calculated at any time. d – g , Observed concentration curves recorded following either saline injection or DEX anesthesia ( d ), KET-XYL anesthesia ( e ), PENTO anesthesia ( f ) and during the waking state or during sleep ( g ). The observed concentrations were significantly lower (two-way ANOVA with Bonferroni–Holm multiple comparisons correction) in the waking state compared to DEX ( P  < 10 −6 ), ketamine-xylazine (KET-XYL) ( P  < 10 −6 ) or pentobarbital (PENTO) ( P  < 10 −6 ) anesthesia or during sleep ( P  < 10 −6 ). The error envelopes represent the s.e.m. h – k , Peak clearance observed following either saline injection or DEX anesthesia ( h ), KET-XYL anesthesia ( i ), PENTO anesthesia ( j ) and during the waking state or during sleep ( k ). For both anesthesia and sleep, the percentage of brain clearance was significantly reduced (two-tailed paired t -test): DEX ( P  = 0.0029), KET-XYL ( P  = 0.0015) or PENTO ( P  = 0.037) anesthesia or during sleep ( P  = 0.016). The vertical bars represent 95% confidence intervals about the mean (horizontal solid lines) and the shaded areas are the distributions of likelihood.

We then repeated these experiments in mice which had been injected (i.p.) with either saline or an anesthetic (Fig. 2d–f ). A comparison was also made between the sleeping and waking states (Fig. 2g ). For the saline controls, the peak concentrations were much lower than that predicted by equation ( 2 ) but could be accounted for accurately by assuming clearance had occurred, as described by equations ( 8 ) and ( 9 ). There was excellent agreement between the photometry data and equation ( 8 ), with the discrepancies at small times possibly being due to dye finding its way across the brain via the ventricles 16 . At the peak concentration (~2–3 h) the clearance was 70–80% with saline-injected controls, indicating that the normal mechanisms of brain clearance had not been disrupted. Notably, in the presence of anesthetics, this clearance was substantially reduced. This was true for dexmedetomidine (Fig. 2d,h ), ketamine-xylazine (Fig. 2e,i ) and pentobarbital (Fig. 2f,j ). Reduced clearance was also observed in mice that were sleeping, compared with mice that were kept awake (Fig. 2g,k and Extended Data Fig. 7 ). By contrast, the diffusion coefficients, reflecting the rate of spread in the brain parenchyma and the time to reach the peak in the photometry data (Fig. 2d–g ), did not change significantly during sleep or anesthesia (Extended Data Table 1 ). If these diffusion coefficients reflect pure diffusion, then they would correspond to a tortuosity of ~1.4. We cannot rule out that spread might be enhanced by local fluid movement without bulk flow; however, these do not change with vigilance state. We also measured the EEG power spectra (Extended Data Fig. 8a–d ) and found a weak negative correlation between peak clearance and delta (0.5–4 Hz) power (Extended Data Fig. 8e ), implying that the deeper the sleep, the lower the clearance.

Histology experiments (Fig. 3 ) confirmed the photometry results. At both 3 h (Fig. 3b, top) and 5 h (Fig. 3b, bottom) after dye injection, the concentration of dye was higher during sleep and ketamine-xylazine anesthesia. As expected, (equation ( 8 )), the spread was Gaussian (fitted curves in Fig. 3b ), with characteristic widths roughly in line with those predicted using the diffusion coefficients derived from the photometry experiments. These data show that redistribution of the AF488 dye is essentially by diffusion alone and confirm that sleep and ketamine-xylazine anesthesia inhibit clearance. Representative brain sections are shown in Fig. 3c at 3 h (top) and 5 h (bottom).

a , At either 3 or 5 h following injection of AF488 into the CPu, the brain was frozen and cryosectioned at 60 μm. The average fluorescent intensity across each slice was obtained by fluorescent microscopy; then the mean intensities across groups of four slices were averaged. b , The mean fluorescence intensity was converted to a concentration using the calibration data in Supplementary Fig. 1 plotted against the anterior–posterior distance from the point of injection for wake (black), sleep (blue) and KET-XYL (red) anesthesia. Top, the data after 3 h. Bottom, the data after 5 h. The lines are Gaussian fits to the data and the error envelopes show the 95% confidence intervals. At both 3 and 5 h, the concentrations during KET-XYL ( P  < 10 −6 at 3 h; P  < 10 −6 at 5 h) and sleep ( P  = 0.0016 at 3 h; P  < 10 −4 at 5 h) were significantly larger than wake (two-way ANOVA with Bonferroni–Holm multiple comparisons correction). c , Representative images of the brain slices across the brain (anterior–posterior distance from the site of AF488 injection) at both 3 h (top three rows) and 5 h (bottom three rows). Each row represents data for the three vigilance states (wake, sleep and KET-XYL anesthesia). The color scale on the right shows the concentrations, determined using the calibration data in Supplementary Fig. 1 .

Our experiments show that brain clearance is reduced during sleep and anesthesia, the opposite conclusion of ref. 3 . Those authors observed that fluorescent dyes injected into the cerebrospinal fluid (CSF) via the cisterna magna penetrated further into the cortex during sleep and anesthesia. They interpreted this as showing that molecular movement into the cortex must be faster during these states. However, the concentration of dye in any brain region will always be the difference between its rate of arrival and its rate of departure and so increased dye penetration in sleep and anesthesia can be equally well explained by a reduced rate of clearance rather than an increased rate of entry. Indeed, almost all the experiments that have been interpreted as showing that sleep or anesthesia change brain clearance have involved introducing markers into the CSF, which then move into the brain parenchyma 14 , 26 , 27 , 28 , 29 , 30 . Under these circumstances, entry, exit and redistribution of the marker are all occurring simultaneously, greatly confounding any quantification of clearance.

Our data in Figs. 2 and 3 show that, averaged across the brain, clearance is reduced by both sleep and anesthesia. Although clearance might vary with anatomical location, the extent of this variation appears small (Extended Data Fig. 9 ). Moreover, the inhibition of clearance by ketamine-xylazine is highly significant independent of location. These data are for a small dye that can freely move in extracellular space. Molecules of larger molecular weights may behave differently. Exactly how anesthetics and sleep inhibit brain clearance is unclear, although it is notable that CSF outflow from the brain is markedly reduced by anesthetics 30 . Whatever the mechanism, however, our results challenge the idea that the core function of sleep is to clear toxins from the brain.

Theoretical basis of three-dimensional photobleaching method

We assume that, following bright illumination, the bleached fluorescent dye is distributed over a hemispherical volume with a concentration, Q ( s ), that falls off as a Gaussian distribution (see main text and Extended Data Figs. 1b and 4b for experimental confirmation):

where Q (0) is the maximum tissue concentration of the bleached dye at the origin of the hemisphere, s is the radial distance from the center of the distribution and σ is the standard deviation of the Gaussian distribution. Then, following bleaching, the concentration C ( r , t ) of bleached dye as a function of time, t , and distance, r , from the center of the hemisphere can be shown to be:

where D is the effective diffusion coefficient governing movement through the tissue. (This result was originally obtained 31 for the case of a spherical ‘volume source’ in the atmosphere and the subsequent diffusion of material from the source.) The effective diffusion coefficient, D , through the tissue is related to the aqueous diffusion coefficient, D aq , by

where the dimensionless parameter λ is the empirical tortuosity, which accounts for the resistance to diffusion and increased path length which a membrane-impermeable dye encounters when diffusing through the tortuous extracellular space 32 .

The fluorescent signal I ( t ) which is recorded at any time t after bleaching is due to unbleached dye diffusing back into the bleached volume. If we assume the volume being recorded from is a hemispherical volume of radius R and that I (0) is the signal recorded immediately after bleaching (at t  = 0) and I (∞) is the signal recorded when equilibrium has been re-established (which is also the signal recorded immediately before bleaching), then M ( t ), the number of moles of bleached dye in the hemispherical volume at a time t , is related to the observed fluorescent intensities by:

where M (0) is the number of moles of bleached dye in the hemisphere immediately following bleaching.

The total number of moles M ( t ) of fluorescent dye in a hemisphere of radius R , is given by equation ( 2 ) multiplied by the area of a hemisphere (2π r 2 ), integrated from 0→ R , which leads to (Extended Data Fig. 2 ):

Hence, as the ratio M ( t )/ M (0) can be determined experimentally (using equation ( 4 )), D can be derived using equation ( 5 ), provided σ and R are known. If we assume that the distance that light penetrates into the tissue to initiate bleaching will be comparable to the distance light penetrates to record the fluorescence as dye diffuses back into the bleached volume, then we can set R  =  σ . In fact, while the time course of M ( t ) is sensitive to values of D and σ , it is insensitive to values of R (Extended Data Fig. 2 ), so this assumption has little impact on the derived value of D .

In the presence of fluid flow with a velocity v , the integral of equation ( 2 ) to give M ( t ) becomes:

The integral cannot be solved analytically but can be evaluated numerically (Extended Data Fig. 5 ).

In vitro photobleaching protocol

The experimental setup is shown in Fig. 1a . Light from a 488-nm laser diode (Doric Lenses) was passed through a 200-μm optical fiber (Doric Lenses) into an agarose gel brain phantom (see ‘Preparation of agarose gel brain phantoms’) containing FITC-dextran (25 mg ml −1 ; Merck Life Science UK). The power at the tip of the optical fiber was measured to be 1.3 mW. Following a 30-s period of photobleaching at 20 °C, controlled by an electronic shutter triggered once every hour, the recovery of fluorescence was recorded using an LED for excitation (465-nm wavelength) and a photoreceiver (New Focus) with a 500–540-nm-wavelength Mini Cube filter) (Doric Lenses). The signal was amplified by a lock-in amplifier (Stanford Research Systems), operating at 125 Hz and stored on a computer. All photometry data were recorded with the software Doric Neuroscience Studio (v.5.4.1.23, Doric Lenses).

In vivo photobleaching protocol

An identical setup was used for the in vivo experiments but with the 200-μm optical fiber being implanted into the frontal cortex of a male C57BL/6J mouse with coordinates: medial–lateral (ML) −1.00 mm, anterior–posterior (AP) 2.22 mm, dorsal–ventral (DV) −2.00 mm and a guide cannula being implanted in the CPu (coordinates: ML −2.55 mm, AP −0.58 mm, DV −3.00 mm) for injection of the 4 kDa FITC-dextran. At the start of the experiment, 4 kDa FITC-dextran was injected into the CPu (25 mg ml −1 in saline; 0.1 μl min −1 over 100 min), with injections being made (with different animals) throughout the 24-h cycle. The dye took about 2 h to be measurable in the frontal cortex, where it reached a peak about 6–7 h after injection (Extended Data Fig. 4a ). Thereafter, there was a slow decline in baseline intensity (~6% per hour), which was corrected for by fitting the baseline to a least-squares cubic spline curve. After ~6 h, the recovery of fluorescence following photobleaching was recorded every hour for up to 24 h.

Measurement of the distribution of bleached dye in agarose gels and the brain

The experimental setup used to measure the distribution of bleached dye from the optical fiber in both agarose gels and the brain is shown in Extended Data Fig. 1a . A brain slice (800 μm) or sheet (800 μm) of an agarose gel brain phantom (see ‘Preparation of agarose gel brain phantoms’) containing FITC-dextran was sandwiched between two 500-μm blocks of clear agarose (0.5% w/v). (The purpose of the blocks of clear agarose was to eliminate internal reflection at the gel–air interfaces which would have existed in their absence, potentially artefactually increasing the spread of light, particularly along the axial direction of the fiber.) An optical fiber (diameter 200 μm) was inserted into the central gel or brain slice and an image taken of the light distribution of a 488-nm laser diode at an intensity which avoided complete bleaching at the center of the distribution. The image was digitized and fit to a hemispherical Gaussian distribution (Extended Data Fig. 1b ). To account for the small spread of the dye during the 30-s bleaching, equation ( 2 ) was integrated over 30 s and this distribution was fit to a Gaussian. This small correction never exceeded 8% (Extended Data Fig. 1c ).

Preparation of agarose gel brain phantoms

Brain phantom gels, to mimic the optical scattering and absorbance of brain tissue, were composed 22 of 1% agarose (Sigma-Aldrich A9539) in phosphate-buffered saline (10 mM phosphate buffer, 2.7 mM KCl and 137 mM NaCl, pH 7.4; Sigma-Aldrich P4417) with 8% dried skimmed milk powder (Sigma-Aldrich 70166) and 0.1% Indian ink (Winsor and Newton 1010754). For validation of the method, 0.3 mg ml −1 of FITC-dextran (molecular weights 4, 10 and 70 kDa) (Sigma-Aldrich 46944, FD10S and 46945, respectively) was added to the brain phantom gel.

Direct measurement of diffusion coefficients in agarose gel brain phantoms

Accurate values of the diffusion coefficients of the FITC-dextran molecules were determined by measuring the efflux of the fluorescent dye from a sheet of agarose gel of known thickness L . If, at t  = 0, a molecule has a uniform concentration of C 0 in a membrane of thickness L and if the membrane is bounded on one side (at x  = 0) by an impermeable barrier, then as the molecule diffuses out of the membrane across the boundary x  =  L , the concentration across the membrane as a function of time is given by 33 :

Because of the cosine term, for values of x that are small compared to L (~20% or less), C ( x , t ) is very insensitive to x . Consequently, if the concentration can be measured close to the impermeable barrier (that is, close to x  = 0), then the time course provides an accurate measurement of D , provided only that L is known.

We constructed 1-mm sheets of 1% agarose gel brain phantoms containing a chosen molecular weight of FITC-dextran (concentration 25 mg ml −1 ), bounded on one side by a glass slide and the other being exposed to a stirred solution of phosphate-buffered saline at a constant temperature (20 °C) containing the same concentrations of milk solids (8%) and India ink (0.1%). A 200-μm optical fiber was inserted immediately adjacent to the impermeable glass slide (so that x/L  = 0.1) (Extended Data Fig. 3 ).

Protocol for measuring brain clearance

For the experiments used to measure brain clearance, a similar experimental arrangement to that described above for bleaching was used (Fig. 1a ), with the same coordinates for the CPu injection and cortical recording. In these experiments, however, we injected a much smaller volume of dye (0.5 μl at 5 mg ml −1 over 10 min) into the CPu and used a smaller dye (AF488) to speed up the dye movement and allow a complete time course to be recorded. After injection, the cannula was capped and the fluorescent intensity recorded in the cortex over several hours. We assumed that the dye spread according to equation ( 8 ) (see Fig. 2 for experimental verification and also Extended Data Fig. 6 ) but where σ is now the characteristic width of the initial Gaussian distribution of dye, rather than the width of the bleached dye, as was the case for the bleaching experiments. To account for the loss of dye due to brain clearance, the equation was multiplied by a term \((1-\frac{t}{t+\tau })\) , where τ is the half time for clearance, giving:

where \(C^{\prime} (r,t)\) is the concentration when clearance is present. The percentage clearance can be calculated from the ratio of the concentrations given by equations ( 2 ) and ( 8 ):

In many cases, the distance r between the optical fiber and the cannula could be measured postmortem but, when this was not available, the calculated distance (3.335 mm) between the two sets of coordinates was used. The average of the measured distances was 3.368 ± 0.064 mm (mean ± s.e.m.; n  = 15).

For the anesthesia experiments, mice were injected with either an anesthetic (see ‘Anesthesia’) or saline, 1 week apart and in random order. For the sleep experiments, mice were sleep deprived for 5 h and then allowed to sleep (Extended Data Fig. 7 ). Recordings were made either during the wake period (for 5 h) or during the recovery sleep period, starting at the first sleep episode. These recordings were made on the same animal, 1 week apart and again in random order.

Calibration of fluorescent intensity

The observed fluorescent intensity was converted to concentration using the data shown in Supplementary Fig. 1 . For both the bleaching experiments and clearance experiments, there were linear relationships between fluorescent intensity and dye concentration. For the bleaching experiments, this was confirmed by measuring fluorescent intensity in solution as a function of concentration of 4 kDa FITC-dextran (Supplementary Fig. 1 ). The solution was that used to prepare the brain phantom gels (see ‘Preparation of agarose gel brain phantoms’). For the clearance experiments, fluorescence was measured either from solutions or from brain slices which had been incubated in different concentrations of dye (Supplementary Fig. 1 ) and imaged as described below for the histology experiments (Fig. 3 ).

All experiments were performed in accordance with the UK Home Office Animal Procedures Act (1986) and all procedures were approved by the Imperial College Ethical Review Committee. Mice used in the experiments were adult male C57/BL6 mice (3–7 months old). Mice were maintained on a 12 h:12 h, light:dark cycle at constant temperature (20 °C) and humidity (50%) with ad libitum food and water. All measurements were made on mice in their home cage.

Stereotaxic surgery

Mice were anesthetized with 2% isoflurane in oxygen by inhalation and received buprenorphine injection (0.1 mg kg −1 subcutaneous (s.c.)) and carprofen (5 mg kg −1 s.c.) and placed in a stereotaxic frame (Angle Two, Leica Microsystems) on a heat mat (ThermoStar Homeothermic Monitoring System, RDW Life Science) at 36.5 °C. Mice were implanted with two miniature screw electrodes (+1.5 mm Bregma, +1.5 mm midline; −2.0 mm Bregma, +1.5 mm midline—reference electrode) with two EMG wires (AS634, Cooner Wire). The EMG electrodes were inserted between the neck musculature. A multipin plug for an EEG–EMG device (see ‘EEG/EMG recording and sleep scoring’) was affixed to the skull with Orthodontic Resin power and Orthodontic resin liquid (TOC Dental). Mice were also implanted with a 200 μm optical fiber (Doric Lenses) in the frontal cortex (coordinates: ML −1.00 mm, AP 2.22 mm, DV −2.00 mm) and a guide cannula for delivering the FITC-dextran or AF488 into the CPu (coordinates: ML −2.55 mm, AP −0.58 mm, DV −3.00 mm). Mice were allowed to recover from surgery for at least 1 week before any experiments were performed.

For the experiments during anesthesia, mice were anesthetized (i.p.) with 200 μg kg −1 (60 μg ml −1 ) dexmedetomidine (Orion Parma), 100 mg kg −1 (20 mg ml −1 ) ketamine (Zeotis) with 20 mg kg −1 (4 mg ml −1 ) xylazine (Dechra) or 50 mg kg −1 (10 mg ml −1 ) pentobarbital (Animalcare), and kept on a heat mat (ThermoStar Homeothermic Monitoring System, RDW Life Science) at 36.5 °C. Control injections were with saline.

EEG/EMG recording and sleep scoring

EEG and EMG signals were recorded using a miniature datalogger attached to the skull 34 . The data were downloaded and waveforms visualized using MATLAB (MathWorks). The EEG signals were high-pass filtered (0.5 Hz, −3 dB) using a digital filter and the EMG was band-pass filtered between 1 and 50 Hz (−3 dB). Power in the delta (1–4 Hz), theta (5–10 Hz) bands and theta to delta band ratio were calculated, along with the root-mean-square value of the EMG signal (averaged over a bin size of 5 s). All of these data were used to define the vigilance states of Wake, NREM sleep and rapid-eye-movement (REM) sleep, initially by an automatic script using a probability-based algorithm and Gaussian Mixture Model (ʻCode Availabilityʼ). The sensitivity and specificity when compared to experienced human sleep scorers were very high (see below). Nonetheless, after automatic scoring, each vigilance state was then screened and confirmed manually afterwards.

Histology experiments

At a chosen time following dye injection into the CPu, mice were killed and their brain taken by dissection and frozen immediately in liquid pentane on dry ice. The brain was then embedded in OCT embedding matrix (CellPath) and kept frozen. Next, the brain was sliced in 60-μm coronal sections using a cryostat (CryoStar NX70, Thermo Fisher Scientific), then immediately dried and mounted on slides using DPX mountant (06522, Sigma-Aldrich). The coronal sections were imaged with a widefield microscope and Zeiss Zen Pro software (Axio Observer, Carl Zeiss) at a magnification of ×5. The average intensity of each slice was measured using ImageJ and the mean intensity in groups of four along the anterior–posterior distance was calculated. The data, when plotted against the anterior–posterior distance from the site of injection, were fitted to Gaussian curves, with variable width, amplitude, baseline and position.

Quantification and statistical analysis

All quantitative results are quoted as means ± 95% confidence intervals or means ± s.e.m. Normality was confirmed using the Kolmogorov–Smirnov test. Comparisons were made using estimation statistics and one-way or two-way analysis of variance (ANOVA). Confidence intervals and sampling distributions (that is, distributions of likelihood) were calculated using bias-corrected and accelerated bootstrapping 35 . The sampling distributions were calculated using 5,000 bootstrap samples. Data collection and analysis were generally not performed blind to the conditions of the experiments. However, the automatic sleep-scoring algorithm was done blind and the vigilance states then checked manually. No statistical methods were used to predetermine sample sizes but our sample sizes are similar to those reported in previous publications 3 , 4 , 12 .

Data exclusions

For the diffusion coefficient measurements, bleaching recordings that could not be fitted by the custom curve-fitting algorithm were excluded. For the photometry recordings, poor fits to the theoretical curves were excluded and recordings where one of the paired recordings (saline or anesthetic; or sleep and wake) was not successful. For the histology experiments, brain sections that were substantially damaged were excluded from the quantitative analysis.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All source data for the main figures and Extended Data figures are available on figshare at https://doi.org/10.6084/m9.figshare.25483339 (ref. 36 ). Source data are provided with this paper.

Code availability

The MATLAB script for automatic sleep scoring is available on figshare at https://doi.org/10.6084/m9.figshare.25483339 (ref. 36 ).

Eban-Rothschild, A. et al. To sleep or not to sleep: neuronal and ecological insights. Curr. Opin. Neurobiol. 44 , 132–138 (2017).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Franks, N. P. & Wisden, W. The inescapable drive to sleep: overlapping mechanisms of sleep and sedation. Science 374 , 556–559 (2021).

Article   CAS   PubMed   Google Scholar  

Xie, L. et al. Sleep drives metabolite clearance from the adult brain. Science 342 , 373–377 (2013).

Iliff, J. J. et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid beta. Sci. Transl. Med. 4 , 147ra111 (2012).

Article   PubMed   PubMed Central   Google Scholar  

Holth, J. K. et al. The sleep–wake cycle regulates brain interstitial fluid tau in mice and CSF tau in humans. Science 363 , 880–884 (2019).

Shokri-Kojori, E. et al. Beta-amyloid accumulation in the human brain after one night of sleep deprivation. Proc. Natl Acad. Sci. USA 115 , 4483–4488 (2018).

Hladky, S. B. & Barrand, M. A. Mechanisms of fluid movement into, through and out of the brain: evaluation of the evidence. Fluids Barriers CNS 11 , 26 (2014).

Louveau, A. et al. Understanding the functions and relationships of the glymphatic system and meningeal lymphatics. J. Clin. Invest. 127 , 3210–3219 (2017).

Tarasoff-Conway, J. M. et al. Clearance systems in the brain–implications for Alzheimer disease. Rev. Neurol. 12 , 248 (2016).

Google Scholar  

Ferris, C. F. Rethinking the conditions and mechanism for glymphatic clearance. Front. Neurosci. 15 , 624690 (2021).

Smith, A. J. et al. Test of the ‘glymphatic’ hypothesis demonstrates diffusive and aquaporin-4-independent solute transport in rodent brain parenchyma. eLife 6 , e27679 (2017).

Mestre, H. et al. Flow of cerebrospinal fluid is driven by arterial pulsations and is reduced in hypertension. Nat. Commun. 9 , 4878 (2018).

Franks, N. P. General anaesthesia: from molecular targets to neuronal pathways of sleep and arousal. Nat. Rev. Neurosci. 9 , 370–386 (2008).

Hablitz, L. M. et al. Increased glymphatic influx is correlated with high EEG delta power and low heart rate in mice under anesthesia. Sci. Adv. 5 , eaav5447 (2019).

Lilius, T. O. et al. Dexmedetomidine enhances glymphatic brain delivery of intrathecally administered drugs. J. Control. Release 304 , 29–38 (2019).

Pla, V. et al. A real-time in vivo clearance assay for quantification of glymphatic efflux. Cell Rep. 40 , 111320 (2022).

Asgari, M. et al. Glymphatic solute transport does not require bulk flow. Sci. Rep. 6 , 38635 (2016).

Holter, K. E. et al. Interstitial solute transport in 3D reconstructed neuropil occurs by diffusion rather than bulk flow. Proc. Natl Acad. Sci. USA 114 , 9894–9899 (2017).

Jin, B. J. et al. Spatial model of convective solute transport in brain extracellular space does not support a ‘glymphatic’ mechanism. J. Gen. Physiol. 148 , 489–501 (2016).

Lu, D. C. et al. Impaired olfaction in mice lacking aquaporin-4 water channels. FASEB J. 22 , 3216–3223 (2008).

Thiagarajah, J. R. et al. Slowed diffusion in tumors revealed by microfiberoptic epifluorescence photobleaching. Nat. Methods 3 , 275–280 (2006).

Zhang, H. & Verkman, A. S. Microfiberoptic measurement of extracellular space volume in brain and tumor slices based on fluorescent dye partitioning. Biophys. J. 99 , 1284–1291 (2010).

Blassle, A. et al. Quantitative diffusion measurements using the open-source software PyFRAP. Nat. Commun. 9 , 1582 (2018).

Pluen, A. et al. Diffusion of macromolecules in agarose gels: comparison of linear and globular configurations. Biophys. J. 77 , 542–552 (1999).

Sykova, E. & Nicholson, C. Diffusion in brain extracellular space. Physiol. Rev. 88 , 1277–1340 (2008).

Benveniste, H. et al. Anesthesia with dexmedetomidine and low-dose isoflurane increases solute transport via the glymphatic pathway in rat brain when compared with high-dose isoflurane. Anesthesiology 127 , 976–988 (2017).

Eide, P. K. et al. Sleep deprivation impairs molecular clearance from the human brain. Brain 144 , 863–874 (2021).

Article   PubMed   Google Scholar  

Gakuba, C. et al. General anesthesia inhibits the activity of the glymphatic system. Theranostics 8 , 710–722 (2018).

Vinje, V. et al. Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation. Fluids Barriers CNS 20 , 62 (2023).

Ma, Q. et al. Rapid lymphatic efflux limits cerebrospinal fluid flow to the brain. Acta Neuropathol. 137 , 151–165 (2019).

Gifford, F. Atmospheric diffusion from volume sources. J. Meterol. 12 , 245–251 (1955).

Article   Google Scholar  

Nicholson, C. Diffusion and related transport mechanisms in brain tissue. Rep. Prog. Phys. 64 , 815–884 (2001).

Article   CAS   Google Scholar  

Crank, J. The Mathematics of Diffusion 1st edn (Clarendon, 1956).

Hsieh, B. et al. A miniature neural recording device to investigate sleep and temperature regulation in mice. In 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) (IEEE, 2019); https://doi.org/10.1109/BIOCAS.2019.8918722

Efron, B. Better bootstrap confidence-intervals. J. Am. Stat. Assoc. 82 , 171–185 (1987).

Miao, A. et al. Source data and custom script for Miao, A. et al. Nature Neuroscience , 2024. figshare https://doi.org/10.6084/m9.figshare.25483339 (2024).

Download references

Acknowledgements

We thank T. Gardner-Medwin and K. Drickamer for useful discussions, K. Tossell, M. Nollet, B. Anuncibay-Soto and K. Jović for assistance and helpful comments on the manuscript. This work was supported by Wellcome Trust grant no. 220759/Z/20/Z (N.P.F., W.W.); the UK Dementia Research Institute (award no. UK DRI-5004) through UK DRI Ltd, principally funded by the Medical Research Council (W.W., N.P.F.); an EPSRC studentship from the EPSRC Centre for Doctoral Training in Neurotechnology (B.H.); and an MRC studentship through the UK DRI (A.M.). The facility for Imaging by Light Microscopy (FILM) at Imperial College London is part-supported by funding from the Wellcome Trust (104931/Z/14/Z) and BBSRC (BB/L105129/1). For the purpose of open access, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission.

Author information

Tianyuan Luo

Present address: Department of Anesthesiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China

Present address: Guizhou Key Laboratory of Anesthesia and Organ Protection, Zunyi Medical University, Zunyi, China

These authors contributed equally: Andawei Miao, Tianyuan Luo.

Authors and Affiliations

Department of Life Sciences, Imperial College London, South Kensington, London, UK

Andawei Miao, Tianyuan Luo, Bryan Hsieh, Christopher J. Edge, Morgan Gridley, Ryan Tak Chun Wong, William Wisden & Nicholas P. Franks

UK Dementia Research Institute, Imperial College London, London, UK

Andawei Miao, William Wisden & Nicholas P. Franks

Centre for Doctoral Training and Centre for Neurotechnology, Imperial College London, London, UK

Bryan Hsieh, William Wisden & Nicholas P. Franks

Department of Electrical and Electronic Engineering and UK Dementia Research Institute, Care Research & Technology, Imperial College London, London, UK

Timothy G. Constandinou

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Contributions

N.P.F. and W.W. conceived the study. A.M., T.L., B.H. and M.G. performed the experiments and with N.P.F. analyzed the data. C.J.E. contributed to the theoretical basis of the methodology. R.T.C.W. calculated the EEG power spectra. T.G.C. contributed to developing data-logging equipment. N.P.F. and W.W. wrote the first draft of the paper and all authors contributed to and approved the final manuscript.

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Correspondence to William Wisden or Nicholas P. Franks .

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Extended data

Extended data fig. 1 measurement of the distribution of bleached dye..

a , A thin brain slice or sheet of an agarose gel brain phantom containing FITC-dextran was sandwiched between two blocks of clear agarose ( Methods ). An optical fiber (core diameter 200 μm) was inserted into the brain slice or central gel and an image taken of the light distribution of a 488 nm-laser diode. b , The intensity distribution from a digitized image (blue lines) was then fit to a hemispherical Gaussian distribution (red solid lines). The average value for the standard deviations of the Gaussian fits was σ  = 149.5 μm (CI [140.7, 162.6]; n  = 8 independent experiments). c , There is a small change in this standard deviation due to diffusion during the 30 s of bleaching (red dashed line), which differs for each molecular weight due to the different diffusion coefficients. This was estimated by averaging the dye distribution (equation [ 2 ]) over 30 s and then fitting this to a Gaussian. Inset: An example of how the dye distribution changes during bleaching for 4 kDa FITC-dextran, D  = 133.9 μm 2 s −1 . The red curve is the Gaussian distribution at the start of bleaching ( σ  = 149.5 μm), the green curve is the average distribution over 30 s, fitted to a Gaussian (blue dashed line) which gives ( σ  = 161.0 μm).). The values of σ that were used for the diffusion measurements in agarose gel for 4 kDa, 10 kDa and 70 kDa FITC-dextran were 152.1 μm, (CI [143.3, 165.0]; n  = 8 independent experiments), 156.2 μm, (CI [147.7, 169.0]; n  = 8 independent experiments) and 161.0 μm, (CI [152.6, 173.2]; n  = 8 independent experiments), respectively.

Extended Data Fig. 2 The time course of M ( t ) (equation [5]) is sensitive to values of D and σ , but insensitive to values of R.

a , The time course of M ( t )/ M (0) for values of D from 20–120 μm 2 s −1 . b , Corresponding half times of M ( t )/ M (0) over the same range of D showing that the half times change greatly with D . c , Corresponding half times of M ( t )/ M (0) over the same range of D showing that the half times change greatly with σ . d , Corresponding half times of M ( t )/ M (0) over the same range of D showing that the half times change little with R . Derivation of equation [ 5 ]. The total number of mols M(t) of fluorescent dye in a hemisphere of radius R, is given by equation [ 2 ] multiplied by the area of a hemisphere (2 πr 2 ), integrated from 0→ R (because we have assumed that the volume being recorded from is a hemisphere of radius R) : \(M(t)=C(0,0){[1+\frac{2Dt}{{\sigma }^{2}}]}^{-\frac{3}{2}}\underset{0}{\overset{R}{\int }}2\pi {r}^{2}exp[\frac{-{r}^{2}}{4Dt+2{\sigma }^{2}}]dr\) This can be written as: \(M(t)=a\underset{0}{\overset{R}{\int }}{r}^{2}exp[-b{r}^{2}]dr\) , where \(a=2\pi C(0,0){[1+\frac{2Dt}{{\sigma }^{2}}]}^{-\frac{3}{2}}\) and \(b={(4Dt+2{\sigma }^{2})}^{-1}\) Integrating by parts gives: \(M(t)=[-\frac{aR}{2b}exp[-b{R}^{2}]]+\underset{0}{\overset{R}{\int }}\frac{a}{2b}exp[-b{r}^{2}]dr\) Using the standard integral: \(\underset{0}{\overset{R}{\int }}exp[-b{r}^{2}]dr=\sqrt{\frac{\pi }{4b}}erf(\sqrt{b}R)\) , we have \(M(t)=\frac{a}{2b}\{\sqrt{\frac{\pi }{4b}}erf(\sqrt{b}R)-Rexp[-b{R}^{2}]\}\) so, finally, substituting in a and b we have Equation [ 5 ]: \(M(t)=\frac{2\pi C(0,0){\sigma }^{3}}{\sqrt{(2Dt+{\sigma }^{2})}}\{\sqrt{\frac{\pi (2Dt+{\sigma }^{2})}{2}}erf(\frac{R}{\sqrt{(4Dt+2{\sigma }^{2})}})-Rexp[-\frac{{R}^{2}}{(4Dt+2{\sigma }^{2})}]\}\) .

Extended Data Fig. 3 Direct measurement of diffusion coefficients.

The diffusion coefficients of the FITC–dextrans in the brain phantom agarose gel were determined directly by measuring the time course of diffusion of FITC-dextran from a 1-mm thick sheet of gel, into an effectively infinite stirred water bath containing all the components of the brain phantom (except the agarose and FITC-dextran). By recording the reduction in the fluorescent signal close to the impermeable glass surface on which the gel was set, as a function of time, the diffusion coefficient could be directly determined using equation [ 7 ] (ref. 26 ). The figure shows data from a typical experiment using 4 kDa FITC-dextran (blue trace) and the red dashed line shows the change predicted by equation [ 7 ].

Extended Data Fig. 4 Measurement of movement in vivo using photobleaching.

a , Fluorescent intensity measured in the frontal cortex following injection of 4 kDa FITC-dextran into the CPu (at t  = 0). After a delay, fluorescent intensity rises to a maximum and then slowly decays. b , As with the experiments in gels, the spread of light in the brain had to be established. This was done using brain slices ( Methods ) and this figure shows a typical image obtained from a brain slice, which provided a measure of the standard deviation of the hemispherical gaussian σ. c , A typical recording in vivo of the recovery of fluorescence after photobleaching. A value for D was derived from the theoretical fit (red dashed line) to Eq. 5 , as described in Methods . d , Throughout the experiment, the EEG and EMG signals were recorded and the power in the delta band (1–4 Hz) and theta band (5–10 Hz) derived, so that the vigilance state (WAKE, NREM or REM) could be determined ( Methods ).

Extended Data Fig. 5 The effect of advective flow on the time course of recovery of fluorescence after photobleaching.

This was assessed in an in vitro experiment illustrated in a . A solution of 4 kDa FITC-dextran was passed through a gel ( Methods ) at a constant flow rate and the time course for the recovery of photobleaching recorded using an optical fiber, exactly as used in the experiments described in the text in vitro and in vivo . b , The observed half times were accurately predicted from equation [ 6 ] and reduced rapidly with increasing advective velocity. From the precision with which we could record changes in diffusion coefficients in vivo (Fig. 1d,e right panels) and their corresponding half times, we estimate that we would have been able to detect a change in advective flow of about 0.5 μm/s, or greater. Where error bars (SEM; n  = 5 independent experiments) are not shown they were smaller than the size of the symbol.

Extended Data Fig. 6 The time course of C(r, t).

a , b According to equation [ 2 ], the concentration at a fixed distance, r , from a Gaussian source (solid lines) reaches a peak with time that depends only on the diffusion coefficient D , while the peak concentration does not change. Almost identical concentrations are predicted if the source is a sphere, rather than a Gaussian, containing the same number of moles (dashed lines). (The equation for the concentration as a function of time from a spherical source has been solved by Crank 26 .) c,d , The peak concentration with time for a fixed diffusion coefficient, decreases with increasing distance r , with relatively small changes in the time to peak.

Extended Data Fig. 7 Vigilance-state percentages for the sleep photometry experiments.

During the sleep experiments, mice were first sleep deprived by placing novel objects in their cage and after 5 hours were then allowed to sleep. The photometry measurements during the WAKE state were carried out during the five hours of sleep deprivation, where the WAKE state occurred, on average, 92% of the time (8% NREM and 0% REM). The photometry measurements during the SLEEP state were carried out after the first sleep episode following sleep deprivation. During the first five hours the vigilance state percentages were: WAKE 9.3% ( n  = 11 mice), NREM 80.8% ( n  = 13 mice), REM 9.9% ( n  = 13 mice), TOTAL SLEEP 90.7% ( n  = 13 mice). Sleep scoring of vigilance states was carried out as described in Methods . The errors bars represent SEMs.

Extended Data Fig. 8 Power spectra during anesthesia and correlation with peak clearance.

Power spectral density plots during anesthesia were calculated for the three anesthetics a-c and d , during recovery sleep. For the anesthetics, the power spectra were carried out using the EEG recorded during the first 2 hours of anesthesia (excluding the first ten minutes following injection). For sleep, the power spectra were calculated during 2 hours of recovery sleep, which included some time in WAKE (9.3%) and REM (9.9%) states. e , There was a weak negative correlation (Pearson’s correlation coefficient −0.58) between delta (0.5–4.0 Hz) power and peak clearance (see Fig. 2d–g and Extended Data Table 1 ). PENTO ( n  = 10 mice), DEX ( n  = 9 mice), SLEEP ( n  = 9 mice) and KET/XYL ( n  = 9 mice). The errors bars represent SEMs and where they are not shown they were smaller than the size of the symbol.

Extended Data Fig. 9 Brain clearance is uniform across the brain.

The concentration of AF488 dye 3 hours after injection into the CPu was measured at an anterior-posterior coordinate 1 mm from the site of injection. a , The concentration of dye was then calculated as a function of radial distance from the peak concentration in both the dorsal and ventral directions. As predicted by equation [ 8 ], this results in a Gaussian curve. b , Using these data (predicted by equation [ 8 ]) together with equation [ 2 ], the percentage clearance can be calculated in the dorsal and ventral directions. Two-way ANOVA shows that there is no significant change in brain clearance across the brain ( p  = 0.99) for both WAKE animals and those anesthetized by ketamine-xylazine. In contrast, the inhibition of clearance by ketamine-xylazine is highly significant ( p  < 10 −6 ). For both panels the means are for n  = 3 animals and the error envelope shows the SEMs.

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Miao, A., Luo, T., Hsieh, B. et al. Brain clearance is reduced during sleep and anesthesia. Nat Neurosci (2024). https://doi.org/10.1038/s41593-024-01638-y

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DOI : https://doi.org/10.1038/s41593-024-01638-y

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