meaning of gap hypothesis

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In This Article Expand or collapse the "in this article" section Knowledge Gap

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Knowledge Gap by Yoori Hwang , Brian Southwell LAST REVIEWED: 29 May 2015 LAST MODIFIED: 29 May 2015 DOI: 10.1093/obo/9780199756841-0078

The essential notion of the knowledge gap is the proposition that a discrepancy exists in the knowledge that people of varying socioeconomic levels attain when engaging mass media content. In other words, the information-rich get richer when reading newspapers or watching television news reports, whereas those with relatively less background knowledge typically gain information at a comparatively lesser rate. The knowledge-gap hypothesis, explicitly formulated by Tichenor, Donohue, and Olien in 1970, goes beyond suggesting a simple knowledge difference between those with more and less formal education. What the hypothesis suggests is not just that there is a gap in knowledge between groups but also that this gap in knowledge widens as more information enters a society. The knowledge gap hypothesis has stimulated communication research in the United States and elsewhere since 1970. So far, researchers have published more than one hundred studies directly considering the knowledge-gap notion, and scholars have widely cited knowledge gap research in many different disciplines.

The knowledge gap hypothesis first appears formally in Tichenor, et al. 1970 . Studying the effects of newspaper readership in Minnesota communities in the 1960s (see Tichenor, et al. 1980 for detail), Phillip J. Tichenor, George A. Donohue, and Clarice N. Olien—an interdisciplinary research team at the University of Minnesota—found that the slope of information uptake was significantly steeper for those with relatively higher educational attainment, such that the gap between individuals with higher and lower education widened over time, though the relationship between newspaper reading and public affairs knowledge was generally positive for all. Education-based knowledge differences are hardly a new phenomenon. In the United States, for example, variation in policy knowledge across the general population has been a cause for concern for much of the 20th and 21st centuries (and earlier). Lippmann 1922 bemoans the inability of most ordinary people to sufficiently understand detailed policy discussions, for example, and Hyman and Sheatsley 1947 notes the existence of “chronic know-nothings,” people who cannot be reached by information campaigns and, consequently, remain chronically uninformed. The notion that people may hold different levels of knowledge as a function of their group membership also animated scholarly works such as Simmel 1955 . What that long line of scholarship suggests about the consequences of such disparities, though, makes the prospect of widening gaps in knowledge quite consequential. For Hyman and Sheatsley 1947 , the potential existence of chronic know-nothings and knowledge inequalities raises a serious issue for democracy insofar as democracy requires an informed citizenry. Moreover, disparity in knowledge is crucial to understanding inequalities in social control and social power in a variety of forums, such as health and science (e.g., Viswanath, et al. 2006 ), and not just in the political arena. Priest 1995 , for instance, points to the information inequity that citizens in the United States faced in the 1980s and 1990s, given that most rely on news reports for information about environmental and health risks, though only some have the advanced education and training necessary to understand risk reports. The extent to which a particular group has knowledge or information likely also affects the extent to which that group can influence political processes and public policymaking. Thus, scholars have had ample motivation in the past fifty years to not only identify knowledge gaps but also to understand conditions under which such gaps might widen, making Tichenor, et al. 1970 a breakthrough of its time.

Hyman, Herbert H., and Paul B. Sheatsley. 1947. Some reasons why information campaigns fail. Public Opinion Quarterly 11:412–423.

DOI: 10.1086/265867

One of the earliest studies that reported the existence of a knowledge gap. The authors argue that certain segments of the public are difficult to inform and discuss the psychological causes of the problem.

Lippmann, Walter. 1922. Public opinion . New York: Harcourt Brace.

Lippmann’s book is a classic for political communication scholars, sounding an important alarm about the general lack of necessary background information to understand key policy debates amongst most of the electorate in the United States.

Priest, Susanna H. 1995. Information equity, public understanding of science, and the biotechnology debate. Journal of Communication 45:39–54.

DOI: 10.1111/j.1460-2466.1995.tb00713.x

Priest’s concept of information equity between groups highlights a potential ethical consideration for knowledge gap research. Moreover, her emphasis on health and science information underscores the importance of not limiting knowledge gap research to politics and public affairs.

Simmel, Georg. 1955. Conflict and the web of group affiliations . Translated by Kurt H. Wolff and Reinhard Bendix. New York: Free Press.

Tichenor himself noted the importance of Simmel’s pioneering work in stimulating thinking about group-level differences in knowledge and knowledge gain. Originally published in 1922.

Tichenor, Phillip J., George A. Donohue, and Clarice N. Olien. 1970. Mass media flow and differential growth in knowledge. Public Opinion Quarterly 34:159–170.

DOI: 10.1086/267786

The seminal article. This article introduces the original knowledge gap hypothesis and presents empirical supports for the hypothesis using public-opinion-polls data and experiment data.

Tichenor, Phillip J., George A. Donohue, and Clarice N. Olien. 1980. Community conflict and the press . Beverly Hills, CA: SAGE.

Explains the Tichenor-Donohue-Olien team’s general research paradigm and how the knowledge gap hypothesis developed. The book discusses the relationships among social conflict, citizens’ media use, and people’s knowledge, based on data from nineteen different communities in Minnesota. Chapter 7 directly discusses the knowledge gap hypothesis.

Viswanath, Kasisomayajula, Nancy Breen, Helen Meissner, Richard P. Moser, Bradford Hesse, Whitney Randolph Steele, and William Rakowski. 2006. Cancer knowledge and disparities in the information age. Journal of Health Communication 11:1–17.

DOI: 10.1080/10810730600637426

Viswanath and colleagues discuss the consequences of information disparities, noting that such gaps are particularly problematic with regard to health and science topics for which equal holding of knowledge might help equalize overall well-being.

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Revisiting the Knowledge Gap Hypothesis: A Meta-Analysis of Thirty-Five Years of Research

This knowledge gap meta-analysis examines (a) average effect size of the gap, (b) impact of media publicity, and (c) moderators of the gap. Positive correlation between education and level of knowledge ( r = .28) was found, with no differences in the size of the gap (a) over time and (b) between issues of higher and lower publicity. However, gap magnitude was moderated by topic, setting, knowledge measure, and study design, but not by publication status, country, and sampling method. Relatively smaller gaps were found for (a) health-science topics compared to social-political topics and (b) local/personal issues compared to international issues.

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Knowledge Gap Theory

Introduction

This theory was first proposed in 1970 by Philip J Tichenor, then Associate Professor of Journalism and mass Communication, George A. Donohue, Professor of Sociology and Clarice. N Olien, Instructor in Sociology, all three researchers in the University of Minnesota. They defined the Knowledge Gap theory, “as the infusion of mass media information into a social system increases higher socioeconomic status segments tend to acquire this information faster than lower socioeconomic status population segments. Hence the gap in knowledge between the two tends to increase rather than decrease.” In simple words , as the access to mass media increases those particular segments of population inevitable gain information faster and hence the wide gap increases with the lower economic status of the population. The world is yet to see the complete effect of the new technologies but as the globe turns out more technological and the expense rises, it more goes out of the league of the poor. As a result the knowledge gap also widens and the people of the higher economic class gain the benefits more. If the Information services are not made equal for the entire society, this gap of information will increase over the years.

In this theory knowledge is treated as any other commodity which is not distributed equally throughout the society and the people at the top of the ladder has more easy access to it. This theory was used in the presidential election and it is was seen that when a new idea invades in the society, the people of the higher strata understand it better and hence the gap expands. But, events such as debates, free talks may help to reduce this gap.

Few reasons have been stated of why this pattern of gap exist

1. Communication Skills – As a person receives more education, his communication skill increases and hence gathering information becomes easier for him. Along with this reading, understanding mad memory skills also become better and thus he understands the issues of various spheres better.

2. Stored information – Via classrooms, textbooks, discussions, educated person is exposed to much more topics than a less educated person and hence his awareness is more.

3. Relevant Social Contact – A person with more education has more social integration. This helps him to counter various perspectives, diverse stories etc which makes his understanding of public issues better.

4. Selective Exposure – An educated person knows well of how to use optimum use of a medium while on the other hand a person with no knowledge is unlikely to know it. Hence he will be less aware of the issues around the world and less interested and may not also know of how it may affect him.

5. Media Target Markets – For every product, news or any commodity a certain segment is targeted and it is usually the higher strata of the society who is targeted and hence the lower strata remains unaware.

Ways of Reducing the Gap

George A. Donohue and his other colleagues by the end of 1975 came up with three variables after a survey on local and national issues, which will help to reduce the gap and that failed this theory upto a certain extent.

Impact of local issues – It was seen that local issues that directly impacted the people had aroused more of social concern than national issues that did not have such a great impact and hence in these issues widened gap could be reduced.
Level of social conflict surrounding the issue – Until a communication breakdown, issues with more perceived conflict tends to grab more attention and weakening the knowledge gap hypothesis.
Homogeneity of the community – If it is a homogeneous community, the gap tends to be lesser than a wider heterogeneous community.

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Good day! my name is Richard Paler, I am a Technology Communication student from Mindanao University Of Science and Technology (MUST) here in the Philippines. I have a question about the Knowledge Gap Theory. Can you explain or simplify assumption of your theory? And also what is the significance of the knowledge Gap theory? I am looking forward for your reply. Thank you and God bless!

Hullo, i like your article, i am a student of communication in University of Jos, Nigeria. Can you relate the knowledge gap assumption with diffusion theory….. Thanks

let me add more details :

Core Assumptions and Statements: The knowledge gap can result in an increased gap between people of lower and higher socioeconomic status. The attempt to improve people’s life with information via the mass media might not always work the way this is planned. Mass media might have the effect of increasing the difference gap between members of social classes. Tichenor, Donohue and Olien presented five reasons for justifying the knowledge gap: 1) People of high socioeconomic status have better communication skills, education, reading, comprehending and remembering information. 2) People of higher socioeconomic status can store information more easily or remember the topic form background knowledge. 3) People of higher socioeconomic status might have a more relevant social context. 4) People of higher socioeconomic status are better in selective exposure, acceptance and retention. 5) The nature of the mass media itself is that it is geared toward persons of higher socioeconomic status.

Media presenting information should realize that people of higher socioeconomic status get their information in a different way than lower educated people. Furthermore, this hypothesis of the knowledge gap might help in understanding the increased gap between people of higher socioeconomic status and people of lower socioeconomic status. It can be used in various circumstances. The methods used in media researches concerned with knowledge gap are surveys of mass media and tests of knowledge.

i really apperciate you for this beneficial work, this theory was given to us as group assignment and through what i gained here it is of high recognition to me, Thanks and God bless

Hi, how can one apply the knowledge gap theory in the context of election sensitization programme. Thank you

can i use knowledge gap theory in my research entitled “An analysis of financial literacy of the members of selected credit cooperative” thank you. i found your theory interesting and good. i like it

what are some of the critics associated with this theory

Can i use knowlege gap theory un my research entitled”the média coverage to health awerenss osques and thé formation of public atittud. Thann you.

what are the criticisms of this theory

Hello… I am a student of mass communication in Cross River University of Technology Calabar. How do I relate this theory to my research work “the percieved improvements in the aesthetics of Nollywood films from 90s till date” thank you. Looking forward to your response.

how we can compare this theory with current situation ………. please elaborate this with current period

It’s very comprehensive.

My name is ALI ,I am a student of Communication Studies of Punjab University Pakistan. Please Can you help me that whats the significance of this theory. how much it influence the number of people..?

I’m a student of mass communication, how can I relate thus theory to my project topic ” impact of commercialization on a news content” I really like your theory, looking forward to your response.

wow great work , thank you sir your work has make my assignment easier than I expected.

Good day! I am Mary Rose, a Communication management student from University of Science and technology of southern Philippines. I am glad to read such a wonderful article like this and i just want to gain more knowledge about this theory and I am also looking for some studies that talks about this . I am looking forward to communicate with you, for me to be able to understand more about this theory. Hoping for your response. Thank you.

I am a student of the department of Mass Communication, University of Benin, Benin city Nigeria. Please can you list out the basic assumption of this theory..

It will require more information to be able to help or assist with answering your question. Your research question will be helpful.

I am wondering about How to apply this theory in social media，like wechat？

my name is favour okwudire a student of mass communication Landmark University, Kwara, Nigeria. first off wonderful article, but can you list out criticism of this theory or criticize the theory please.

how can i relate this knowledge gap with online education issues in pakistan during covid 19 because i through due to online education knowledge gap increase in pakistan

The knowledge gap theory holds that with time, the information gap between the rich and poor will be eventually closed. True or false

Thank you for the write up

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

Research Methodology

Qualitative Data Coding

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Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

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.

A good research hypothesis needs to be very clear about what’s being assessed and very specific about the expected outcome.

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 🙂

A good research hypothesis must be testable. In other words, you must able to collect observable data in a scientifically rigorous fashion to test it.

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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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
Education : “Implementing a new teaching method will result in higher student achievement scores.”
Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Evaluating Research – Process, Examples and...

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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.

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Quantitative Biology > Neurons and Cognition

Title: an investigation of conformal isometry hypothesis for grid cells.

Abstract: This paper investigates the conformal isometry hypothesis as a potential explanation for the emergence of hexagonal periodic patterns in the response maps of grid cells. The hypothesis posits that the activities of the population of grid cells form a high-dimensional vector in the neural space, representing the agent's self-position in 2D physical space. As the agent moves in the 2D physical space, the vector rotates in a 2D manifold in the neural space, driven by a recurrent neural network. The conformal isometry hypothesis proposes that this 2D manifold in the neural space is a conformally isometric embedding of the 2D physical space, in the sense that local displacements of the vector in neural space are proportional to local displacements of the agent in the physical space. Thus the 2D manifold forms an internal map of the 2D physical space, equipped with an internal metric. In this paper, we conduct numerical experiments to show that this hypothesis underlies the hexagon periodic patterns of grid cells. We also conduct theoretical analysis to further support this hypothesis. In addition, we propose a conformal modulation of the input velocity of the agent so that the recurrent neural network of grid cells satisfies the conformal isometry hypothesis automatically. To summarize, our work provides numerical and theoretical evidences for the conformal isometry hypothesis for grid cells and may serve as a foundation for further development of normative models of grid cells and beyond.

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At the centre of the image is a nebula on the black background of space. The nebula is composed of clumpy, red, filamentary clouds. At the centre-right of the red clouds is a large cavernous bubble, and at the centre of the bubble there is an opaque blue glow with speckles of stars. At the edges of the bubble, the dust is white. There are several other smaller cavernous bubbles at the top of the nebula. There are also some smaller, red stars and a few disc-shaped galaxies scattered about the image.

What if aliens exist—but they're just hiding from us? The Dark Forest theory, explained

The chilling theory depicted in the Netflix series ‘3 Body Problem’ is just one explanation for our lack of encounters with extraterrestrial intelligence.

The famed Fermi paradox has bewitched astronomers for more than half a century. To put it concisely: If the cosmos is nearly 14 billion years old, then where are all the interstellar societies? Why haven’t they popped over to say hello? Myriad solutions to this conundrum have been proposed, but perhaps none more chilling than the Dark Forest theory.

As the supposition holds, the reason we can’t see these alien civilizations is because they’re all in hiding. Unlike humanity—whose radio transmissions have long echoed throughout our local galactic neighborhood—these societies have all concluded that it’s simply too dangerous to broadcast their location to potentially hostile neighbors.

It's a sobering thought—and one that’s gaining attention now that it’s being depicted in 3 Body Problem , a Netflix adaptation of author Cixin Liu’s literary trilogy. But is it a plausible solution to the Fermi paradox? Of all the posited answers, experts say the Dark Forest hypothesis seems less likely to be correct.

It’s possible that several extraterrestrial intelligences, or ETIs, would conceal themselves. But it’s improbable that all of them will come to the same fear-based conclusion and hide away.

( In the hunt for alien life, this planet just became a top suspect .)

“We don’t even see that same behavior on cultures here on Earth,” says Moiya McTier , an astrophysicist, author, and folklorist. Some ETIs might have members that all act in perfect unison. But others will have divergent, independently behaving groups—some who trend more toward aggression or pacifism, curiosity, or reclusiveness. If one of them waves hello, then that Dark Forest will get a brightly lit campfire for us to see.

For Hungry Minds

But technically anything is possible considering we don’t have any evidence for ETIs to begin with. Perhaps everyone really is hiding. Maybe there truly is a threat lurking out there, somewhere in the dark. And maybe humanity just hasn’t realized it yet.

The case for the Dark Forest theory

The Fermi Paradox was casually raised by physicist Enrico Fermi during a lunchtime chat way back in 1950. It has many nuances, but at its heart is this central premise: Our solar system is just 4.6 billion years old, whereas the universe is 13.8 billion years old. That is plenty of time for life on other planets to develop into technologically advanced societies, those that could set forth across the sea of stars and create outposts or new societies on countless worlds.

But we have yet to find any sign of these societies. So where is everybody?

“There are so many possible overlapping solutions to the Fermi paradox,” says McTier. Is space simply too vast for alien societies to have reached Earth yet? Do all of them destroy themselves before becoming interstellar? Are we the only technologically advanced society in our corner of the cosmos? Is the evolution of life vanishingly rare?

( This man launched the quest to find alien intelligence. It changed astronomy .)

“All the Fermi paradox tells you is that civilizations are rare. It doesn’t tell you why they’re rare,” says Ian Crawford , a planetary scientist and astrobiologist at Birkbeck, University of London. “One of the solutions is: yeah, they’re all out there, but they’re all hiding. If they give themselves away, someone will come and destroy them.”

The idea that these spacefaring aliens are simply reluctant to reveal themselves has featured in sci-fi storytelling for many decades . Liu, in his 2008 book, gave the hypothesis a catchy name. He describes the universe as a dark forest , wherein each alien society is like a fearful, armed hunter gingerly moving forth. If that hunter finds “other life—another hunter, an angel or a demon, a delicate infant or a tottering old man, a fairy or a demigod—there’s only one thing he can do: open fire and eliminate them. In this forest, hell is other people.”

Being fearful has its evolutionary benefits: We may flinch at a strange noise in the night, and although most of the time it’s harmless, our caution may save our lives the one time it’s coming from a genuine threat.

“It can’t be denied that there is some survival value in being aggressive,” Seth Shostak , a senior astronomer at the Search for Extraterrestrial Intelligence Institute in California. Preemptively take out the competition, and you may sleep more securely while getting extra resources. The history of humanity—and its present—is littered with grim examples of this.

A massive cluster of bright, yellow and white light stars.

The case against the Dark Forest theory

Thankfully, the Dark Forest has a plethora of issues that are difficult to resolve—the most obvious being that it’s extremely difficult to conceal a technologically advanced world.

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Long before actively searching for ETIs became a global scientific practice , radio signals from Earth’s quotidian intraspecies communications have been emanating into the void—something that a nearby alien society hoping to find a new ally, or fresh target, could handily spot.

( How many alien civilizations are out there? A galactic survey holds a clue .)

Even as we’ve begun to grasp the hypothetical threat, it's not like we’re about to go completely silent, either. “We’ve never given the slightest thought to turning off all the radars because it might be dangerous,” says Shostak. “It’s just not gonna happen.”

Even if an ETI tried to conceal itself, it may not be sophisticated enough to work. Some alien societies may have found a way to stamp out all their noise, but others may be accidentally still giving the game away without realizing. “The way cavemen might hide is quite different from the way Klingons might hide,” says Shostak.

The forest analogy also falls apart when you consider the true nature of the universe—or simply our own ginormous galaxy. The woods can seem huge and endless in the dark, but that’s peanuts compared to space.

“There may be hostile aliens out there,” says Shostak. But the distances between them are likely to be unfathomably vast, so much so that the idea they would feel the need to preemptively attack one another seems odd. Even if they feared each other, the expanse between them means that they wouldn’t likely need to compete for resources; each would have near-limitless worlds, asteroids, and even stars to exploit.

The fact that Earth is, by universal standards, a young, noisy, and vulnerable technological society, also by default implies that—if there are ETIs out there—then they cannot all be instinctively aggressive.

“If there are so many civilizations, and some of them could destroy us, then we have to explain how that has not happened,” says Karim Jebari , a researcher at the Institute for Futures Studies in Stockholm, Sweden. “Maybe there’s a Galactic Empire that keeps the hostilities down, or maybe it's really difficult… to attack each other over interstellar distances.”

( What we know from decades of UFO government investigations .)

Or, as Jebari has suggested in a recent paper , ETIs have reached the same logical conclusion: that they still exist because other advanced alien societies have chosen not to smite them, perhaps hoping instead to have a mutually beneficial conversation. “We have no reason to attack them in a preemptive strike,” says Jebari. “If they’re smart… maybe they’re thinking the same thing about us.”

That all ETIs would share the very human instinct of assuming the worst about an unknown entity is also a massive presumption.

“For me, [the Dark Forest] is one of the less compelling explanations for the Fermi paradox, because it relies on a few anthropocentric assumptions that I don’t think are fair,” says McTier. Fear is a powerful thing. But so is curiosity.

The nightmare scenario

That doesn’t necessarily mean that the Dark Forest hypothesis is a nonstarter. The problem is that addressing the holes in the theory requires amping up the terror factor.

“The nightmare scenario is that suppose those that are hiding are right,” says Crawford. “Suppose that, sometime in the history of the galaxy, a technological civilization… decided that whenever planets with life or technology were found, they were going to destroy it.”

In other words, if extermination for extermination’s sake was the goal, then the Dark Forest seems more plausible. “If something like that has been going on in the history of the galaxy, then yes it would explain the Fermi paradox,” Crawford says.

So sure, maybe our corner of the cosmos is quiet because life getting started in the first place is an extreme rarity. Perhaps it’s lonely out here because alien societies have a bad habit of annihilating themselves once they discover something like atomic weapons.

Or, just maybe, “we don’t see them because they’re not there,” says Crawford—because a slaughtering entity is going from star to star extinguishing any sign of life. “That’s the really scary thing.”

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CT’s Puerto Ricans continue to face disparities, research reveals

Guests gather for the Festival del Coqui following the Greater Hartford Puerto Rican Day Parade Sunday Oct. 8, 2023. Thousands marched and gathered along the parade route that finished in Bushnell Park, in the shadow of the Connecticut State Capitol.

New research from the Puerto Rican Studies Initiative at UConn shows changing dynamics within Connecticut's Hispanic community and other socioeconomic disparities.

Despite some improvement since 2000, Puerto Ricans in Connecticut still have the highest poverty rates in the state, particularly among women, according to the research conducted by Luis Palomino , a UConn graduate student, and Charles Venator Santiago , associate professor of political science and director of the Puerto Rican Studies Initiative at UConn.

“I see a lot of women in what I think is a position of power,” Venator Santiago said. But, “when I look at the data, the numbers are really low. Salaries are low. Poverty is higher. A lot more single women who are heads of the household. Unless I look at the data, I wouldn't think that there are inequalities.”

Between 2000 and 2022, Connecticut’s white population declined by 13%, while the Puerto Rican population grew by 46%. The Hispanic or Latino population, excluding Puerto Ricans, increased by 171%.

In 2022, Puerto Ricans made up 8% of the state population while other Hispanics or Latinos made up 10%. Together both groups are the second-largest ethnic group in the state of Connecticut after whites.

According to the study, the majority of Puerto Ricans and Hispanic or Latinos are young and entering the labor force at a higher rate than other groups in the state.

In fact, between 2000 and 2022, the highest employment rates corresponded to Hispanics or Latinos, excluding Puerto Ricans, followed by Asians, and whites. But Puerto Ricans experienced the lowest employment rates.

Puerto Ricans as a group experienced the most significant decline in poverty among all populations in Connecticut between 2000 and 2022. Despite that progress, Puerto Ricans still had the highest rates of poverty among all groups.

Household income for people who identified as Puerto Rican in Connecticut was roughly half that of whites and Asians. The study shows they are concentrated in lower-paying service and office jobs, while Hispanics or Latinos, despite higher employment rates, also occupy many service positions.

“So we're estimating that a lot more Puerto Ricans and Latinos are settling in Connecticut because the cost of living is a little cheaper than other states. Well, I mean a couple of hundred dollars,” Venator explains. “But when you're living paycheck to paycheck, you know, $200 or $300 might make a difference.”

Palomino says that homeownership, a major avenue for wealth accumulation, is less accessible to Puerto Ricans, compared to other Hispanics or Latinos who earn slightly more income. But both groups face higher mortgage and rent burdens due to rising housing costs.

"The value of properties is very high,” Palomino said. “Inflation is rising, and wages are low. As a result, when several family members live together, all the income goes towards utilities. This leaves them without enough money to buy a house.”

Puerto Ricans had the lowest college completion rates of all populations in Connecticut and this gap contributes to challenges in securing higher-paying jobs.

Despite the growing Hispanic or Latino population overall, Venator pointed out a significant gap in political participation. "Puerto Ricans and Latinos account for almost 20% of Connecticut's population, but their voting-age population is only around 13%," Venator said.

Both authors said there’s disillusionment among younger Latinos and Americans with the political process.

“There are several hypotheses. Both groups live with low incomes and are focused on trying to survive day-to-day, so participating in politics is not a priority,” Palomino says. “Another hypothesis is the low levels of education, as they may not be aware of the disadvantages or advantages they could have.”

“Nothing has changed since 2000. Poverty has decreased for Puerto Ricans and Latinos, but they're still at the bottom of the poverty scale,” Venator Santiago said. “Whether there was a blip in change after Black Lives Matter and George Floyd, the pandemic, yes, there are some blips, but overall, not much has changed historically.”

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Home Prices in Swing States Could Decide the 2024 Election—and the Reason Might Surprise You

Home prices could play a subtle but important role in the 2024 presidential election, according to a recent first-of-its kind study.

The academic study, Housing Performance and the Electorate , analyzed home prices and election results at the county level for each of the six presidential elections from 2000 to 2020.

The authors found that local home price performance significantly affects voting in presidential elections at the county level. Counties with superior gains in home prices in the four years preceding an election were more likely to “vote-switch” to the incumbent party’s presidential candidate.

Conversely, counties with relatively inferior home price performance leading up to an election were more likely to flip their vote to support the candidate challenging the incumbent party.

In other words, quickly rising home prices tend to favor the incumbent president’s party, whether it be the Democrats or Republicans. The study found that the relationship is strongest in the years closest to an election, and that home prices were most influential in the small group of “swing counties” with a history of switching party preference.

University of Alabama Associate Professor of Finance Alan Tidwell , one of the study’s co-authors, explains that the logic driving this trend is simple: For most voters, their home represents their single largest asset.

“People feel more financially wealthy if they have a lot of housing equity, relative to lower housing equity,” he says. “How financially wealthy they feel really impacts their sense of financial and economic well-being.”

For the upcoming presidential election, the new finding suggests the outcome could be partly influenced by home prices in swing counties of the seven battleground states: Arizona, Georgia, Michigan, Nevada, North Carolina, Pennsylvania, and Wisconsin.

“In the swing counties, they care about this economic factor most, and real estate is one of the main drivers of household wealth,” says lead author Eren Cifci , an assistant professor of finance at Austin Peay State University in Tennessee. “So there may be many other factors that affect how people vote, but this definitely appears to be one of the factors influencing voters when they make their decisions.”

Explaining the 'homevoter hypothesis'

The study’s finding is an extension of the “homevoter hypothesis,” which holds that homeowners tend to vote in support of policies and candidates they believe will boost their home values.

While that phenomenon is well documented in local politics, where government policies have the clearest impact on home values, the new study is the first to show evidence of homevoter behavior in national elections.

The term “homevoter” was coined in 2001 by William A. Fischel , a now-retired economics professor at Dartmouth College and expert in local government and land use regulation.

Fischel conceived the homevoter hypothesis while serving on the local zoning board in Hannover, NH. Regularly, he would hear objections and concerns about zoning changes that seemed esoteric, and noticed that the complaints were always from homeowners.

Fischel says he came to realize that homeowners are essentially shareholders in their community, similar to owners of stock in a company—but that unlike corporate shareholders, they cannot easily diversify their portfolio or liquidate their holdings.

“It's people who are voting their homes, and that's actually an old concept in economics,” says Fischel. “But also, they're very risk-averse, because so much of their assets are stuck in one stock, in one place.”

Fischel says he was surprised by the recent study linking home prices and voting in national elections, since he had always viewed homevoting as primarily a local phenomenon.

“I can see, a little bit, what a presidential election might mean for home values. But it's so indirect, I was really quite surprised at the strength of the evidence,” he says. “How did they find such a strong mechanism? But I have no reason to doubt their evidence.”

For his part, Tidwell argues that the economy plays a major role in most presidential elections, and that rising home equity has a significant impact on how voters perceive the strength of the economy.

Even if local policies, such as zoning laws and public school funding, have a bigger direct impact on local home prices, national elections are where more voters take the opportunity to weigh in with their concern or satisfaction, he says.

“Local elections don't have big turnout, and they don't have big visibility, whereas the national election has a whole bunch more turnout and a whole lot more national media exposure, especially with talk of the economy,” says Tidwell.

Home prices play the biggest role in swing counties

To conduct their study, Cifci and Tidwell, with co-authors Sherwood Clements and Andres Jauregui , looked at the voting results for every county in the continental U.S. over the past six presidential elections.

Of those counties, 77% never changed their party preference, voting for either the Democrat or the Republican in every election since 2000, which was used as the base year for analyzing the 2004 election.

But 641 counties across the country–or 23%–switched their party vote at least once across the survey period, some as many as four times. In that subset of swing counties, home prices appeared to have the biggest impact on election results, according to the study.

In swing counties, for every 1% increase in home values over the four years preceding an election, the county was 0.36% more likely to vote for the incumbent party in the next election, the study found.

As well, the data showed that each 1% increase in home prices made the county 0.19% more likely to “flip” its vote to the incumbent party’s candidate. Those figures are after the study controlled for a variety of other factors that could sway elections, such as changes in demographics, the economy, and government benefits.

“The larger the return [on home values], the more likely you are to vote for the incumbent, or to flip for the incumbent,” explains Tidwell. “For every percent of positive return, there is a percentage increase in voting for the incumbent.”

What does it mean for 2024?

Home prices have risen rapidly across the country over the past four years, including in the seven swing states.

From March 2020 to March 2024, national home values rose 46.4%, according to the Freddie Mac Home Price Index. Of the swing states, North Carolina, Arizona, Georgia, and Wisconsin all outperformed the national average, with four-year price gains greater than 50%.

The study suggests that trend would tend broadly to favor the incumbent, President Joe Biden , as he seeks reelection, particularly in the areas that have seen the strongest home price gains. But the authors caution that their finding only demonstrates a statistical nudge in one direction or the other. They warn that there are many other variables at play in an election.

“It's just one of many factors,” Tidwell says of home price performance. “It's not really a forecast on its own.”

As well, voter turnout in counties that are reliably Democratic or Republican can be just as important to the state-level results in swing states as the marginal shifts in counties that flip from one party to another.

But in an election that is increasingly focused on the housing market, the new findings provide an interesting twist on the role of home prices in voter decision making.

Donald Trump , the presumptive Republican nominee, and his allies have recently levied attacks against Biden over rising home prices, pointing to the challenges raised for prospective first-time homebuyers.

“Under President Biden, home prices have risen almost 50%, making it nearly impossible for millennials to buy their first home and driving the American Dream further and further out of reach,” wrote Sen. Tim Scott , a South Carolina Republican and staunch Trump supporter, on the social media platform X.

On his own Truth Social platform, Trump himself recently wrote: “Crooked Joe has made it impossible for millions of Americans, especially YOUNG Americans, to buy a home.” (Conversely, Trump has also accused Biden of trying to “destroy your property values” by abolishing single-family zoning in the suburbs. The two arguments seem difficult to reconcile.)

It’s true that rising home prices, along with high mortgage rates, are key factors in a national housing crisis that has pushed ownership out of reach for many prospective homebuyers. But on the flip side, most voters are already homeowners. The U.S. homeownership rate is about 66%, and homeowners are significantly more likely to vote than renters.

For existing homeowners, rising home prices mean more equity and higher household net worth, the same as what rising stock prices mean for shareholders.

It suggests that for Republicans, attacking Biden over rising home prices might not carry the same weight with voters as criticism over inflation for goods such as gasoline and groceries.

“When you go to the grocery store or restaurant or the gas pump, I think maybe people feel a little bit different pain than if they own a house and they see their house price going up,” says Tidwell.

On the other hand, the study found evidence that, for swing counties, the economically rational choice might be to always flip to the non-incumbent party, which in 2024 would be the Republicans.

The study found that counties that flipped their vote to an incumbent party candidate were not rewarded with superior home price returns in the four years after the election.

However, counties that flipped to vote for the non-incumbent did experience “positive and significant post-election housing returns” if that candidate won. The authors speculate that this might be due to the winning party rewarding new supporters by increasing investment in those areas after regaining the White House.

“The counties that make the national results flip parties, they do well,” says Clements, a collegiate assistant professor of real estate at Virginia Tech. “Whatever counties voted for Biden last time and vote for Trump this time, if you believe our research, they’re going to have home prices rising if Trump wins.”

Editor's note: This article is part of a special Realtor.com series on the housing market and the swing states in the 2024 presidential election. For additional coverage in this series, click here .

Keith Griffith is a journalist at Realtor.com. He covers the housing market and real estate trends.

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Rethinking the sun's cycles: New physical model reinforces planetary hypothesis

R esearchers at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the University of Latvia have posited the first comprehensive physical explanation for the sun's various activity cycles. It identifies vortex-shaped currents on the sun, known as Rossby waves, as mediators between the tidal influences of Venus, Earth as well as Jupiter and the sun's magnetic activity.

The researchers therefore present a consistent model for solar cycles of different lengths—and another strong argument to support the previously controversial planetary hypothesis. The results have now been published in the journal Solar Physics .

Although the sun, being near to us, is the best researched star, many questions about its physics have not yet been completely answered. These include the rhythmic fluctuations in solar activity. The most famous of these is that, on average, the sun reaches a radiation maximum every eleven years—which experts refer to as the Schwabe cycle.

This cycle of activity occurs because the sun's magnetic field changes during this period and eventually reverses polarity. This, in itself, is not unusual for a star—if it weren't for the fact that the Schwabe cycle is remarkably stable.

The Schwabe cycle is overlaid by other, less obvious fluctuations in activity ranging from a few hundred days to several hundred years, each named after their discoverers. Although there have already been various attempts to explain these cycles and mathematical calculations, there is still no comprehensive physical model.

Planets set the beat

For some years, Dr. Frank Stefani of HZDR's Institute of Fluid Dynamics has been an advocate of the "planetary hypothesis" because it is clear that the planets' gravity exerts a tidal effect on the sun, similar to that of the moon on the Earth. This effect is strongest every 11.07 years: whenever the three planets Venus, Earth and Jupiter are aligned with the sun in a particularly striking line, comparable to a spring tide on Earth when there is a new or full moon. This coincides conspicuously with the Schwabe cycle.

The sun's magnetic field is formed by complex movements of the electrically conducting plasma inside the sun. "You can think of it like a gigantic dynamo. While this solar dynamo generates an approximately 11-year activity cycle in its own right, we think the planets' influence then intervenes in the workings of this dynamo, repeatedly giving it a little push and thus forcing the unusually stable 11.07-year rhythm on the sun," Stefani explains.

Several years ago, he and his colleagues discovered strong evidence of a clocked process of this kind in the available observation data. They were also able to correlate various solar cycles with the movement of the planets just using mathematical methods. At first, however, the correlation could not be sufficiently explained physically.

Rossby waves on the sun act as intermediaries

"We have now found the underlying physical mechanism. We know how much energy is required to synchronize the dynamo, and we know that this energy can be transferred to the sun by so-called Rossby waves. The great thing is that we can now not only explain the Schwabe cycle and longer solar cycles but also the shorter Rieger cycles that we hadn't even considered previously," says Stefani.

Rossby waves are vortex-shaped currents on the sun similar to the large-scale wave movements in the Earth's atmosphere that control high- and low-pressure systems.

The researchers calculated that the tidal forces during the spring tides of two of each of the three planets Venus, Earth and Jupiter had exactly the right properties to activate Rossby waves—an insight with many consequences.

First of all, these Rossby waves then achieve sufficiently high speeds to give the solar dynamo the necessary impetus. Second, this occurs every 118, 193 and 299 days in accordance with the Rieger cycles that have been observed on the sun. And thirdly, all additional solar cycles can be calculated on this basis.

All cycles explained by a single model

This is where mathematics comes in: The superimposition of the three short Rieger cycles automatically produces the prominent 11.07-year Schwabe cycle. And the model even predicts long-term fluctuations of the sun because the movement of the sun around the solar system's center of gravity causes a so-called beat period of 193 years on the basis of the Schwabe cycle.

This corresponds to the order of magnitude of another cycle that has been observed, the Suess-de Vries cycle.

In this context, the researchers discovered an impressive correlation between the 193-year period that had been calculated and periodic fluctuations in climate data. This is another robust argument for the planetary hypothesis because "the sharp Suess-de Vries peak at 193 years can hardly be explained without phase stability in the Schwabe cycle, which is only present in a clocked process," Stefani estimates.

Does this mean the question as to whether the sun follows the planets' beat has finally been answered? Stefani says, "We'll probably only be 100% certain when we have more data. But the arguments in favor of a process clocked by the planets are now very strong."

More information: F. Stefani et al, Rieger, Schwabe, Suess-de Vries: The Sunny Beats of Resonance, Solar Physics (2024). DOI: 10.1007/s11207-024-02295-x

Provided by Helmholtz Association of German Research Centres

The sun is currently approaching a maximum activity in the 11-year "Schwabe cycle" again, here a Solar Orbiter image from October 2023. Credit: ESA & NASA/Solar Orbiter/EUI Team

COMMENTS

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The knowledge gap hypothesis proposes that, as more and more information is disseminated into a social system such as a community or a nation, the "haves" gain more knowledge faster than the "have nots" so that relative differentials in knowledge between them increase, both at one point in time and over time. The hypothesis has mainly been applied to scientific and public affairs ...
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Knowledge Gap by Yoori Hwang , Brian Southwell LAST REVIEWED: 29 May 2015 LAST MODIFIED: 29 May 2015 DOI: 10.1093/obo/9780199756841-0078

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What is a Hypothesis – Types, Examples and Writing Guide

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