scientific hypothesis form

What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

Sign up for the Live Science daily newsletter now

Get the world’s most fascinating discoveries delivered straight to your inbox.

Warm ocean water is rushing beneath Antarctica's 'Doomsday Glacier,' making its collapse more likely

Earth from space: Rare phenomenon transforms African thunderstorm into giant ethereal 'jellyfish'

Scientists grow diamonds from scratch in 15 minutes thanks to groundbreaking new process

Most Popular

• 2 James Webb telescope confirms there is something seriously wrong with our understanding of the universe
• 3 Earth from space: Rare phenomenon transforms African thunderstorm into giant ethereal 'jellyfish'
• 4 Some of the oldest stars in the universe found hiding near the Milky Way's edge — and they may not be alone
• 5 See May's full 'Flower Moon' rise this week close to a red supergiant star
• 2 Scientists finally solve mystery of why Europeans have less Neanderthal DNA than East Asians
• 3 'We are approaching the tipping point': Marker for the collapse of key Atlantic current discovered
• 4 Can a commercial airplane do a barrel roll?
• 5 Why is there sometimes a green flash at sunset and sunrise?

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

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

Prevent plagiarism, run a free check.

Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

Cite this Scribbr article

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

McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 21 May 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

Is this article helpful?

Shona McCombes

Other students also liked, operationalisation | a guide with examples, pros & cons, what is a conceptual framework | tips & examples, a quick guide to experimental design | 5 steps & examples.

• Science Notes Posts
• Contact Science Notes
• Todd Helmenstine Biography
• Anne Helmenstine Biography
• Free Printable Periodic Tables (PDF and PNG)
• Periodic Table Wallpapers
• Interactive Periodic Table
• Periodic Table Posters
• How to Grow Crystals
• Chemistry Projects
• Fire and Flames Projects
• Holiday Science
• Chemistry Problems With Answers
• Physics Problems
• Unit Conversion Example Problems
• Chemistry Worksheets
• Biology Worksheets
• Periodic Table Worksheets
• Physical Science Worksheets
• Science Lab Worksheets
• My Amazon Books

Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it takes longer for people to fall asleep.
• If you refrigerate apples, they last longer before going bad.
• If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
• If you leave a bucket of water uncovered, then it evaporates more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

• Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
• Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
• Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
• Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

Related Posts

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

Psst... there’s more!

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

You Might Also Like:

16 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

Trackbacks/Pingbacks

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

Submit a Comment Cancel reply

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

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

• Print Friendly

• Privacy Policy

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

You may also like

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

Institutional Review Board – Application Sample...

Evaluating Research – Process, Examples and...

An official website of the United States government

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

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

• Publications
• Account settings

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

• Advanced Search
• Journal List
• J Korean Med Sci
• v.36(50); 2021 Dec 27

Formulating Hypotheses for Different Study Designs

Durga prasanna misra.

1 Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.

Armen Yuri Gasparyan

2 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, UK.

Olena Zimba

3 Department of Internal Medicine #2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Vikas Agarwal

George d. kitas.

5 Centre for Epidemiology versus Arthritis, University of Manchester, Manchester, UK.

Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. Observational and interventional studies help to test hypotheses. A good hypothesis is usually based on previous evidence-based reports. Hypotheses without evidence-based justification and a priori ideas are not received favourably by the scientific community. Original research to test a hypothesis should be carefully planned to ensure appropriate methodology and adequate statistical power. While hypotheses can challenge conventional thinking and may be controversial, they should not be destructive. A hypothesis should be tested by ethically sound experiments with meaningful ethical and clinical implications. The coronavirus disease 2019 pandemic has brought into sharp focus numerous hypotheses, some of which were proven (e.g. effectiveness of corticosteroids in those with hypoxia) while others were disproven (e.g. ineffectiveness of hydroxychloroquine and ivermectin).

Graphical Abstract

DEFINING WORKING AND STANDALONE SCIENTIFIC HYPOTHESES

Science is the systematized description of natural truths and facts. Routine observations of existing life phenomena lead to the creative thinking and generation of ideas about mechanisms of such phenomena and related human interventions. Such ideas presented in a structured format can be viewed as hypotheses. After generating a hypothesis, it is necessary to test it to prove its validity. Thus, hypothesis can be defined as a proposed mechanism of a naturally occurring event or a proposed outcome of an intervention. 1 , 2

Hypothesis testing requires choosing the most appropriate methodology and adequately powering statistically the study to be able to “prove” or “disprove” it within predetermined and widely accepted levels of certainty. This entails sample size calculation that often takes into account previously published observations and pilot studies. 2 , 3 In the era of digitization, hypothesis generation and testing may benefit from the availability of numerous platforms for data dissemination, social networking, and expert validation. Related expert evaluations may reveal strengths and limitations of proposed ideas at early stages of post-publication promotion, preventing the implementation of unsupported controversial points. 4

Thus, hypothesis generation is an important initial step in the research workflow, reflecting accumulating evidence and experts' stance. In this article, we overview the genesis and importance of scientific hypotheses and their relevance in the era of the coronavirus disease 2019 (COVID-19) pandemic.

DO WE NEED HYPOTHESES FOR ALL STUDY DESIGNS?

Broadly, research can be categorized as primary or secondary. In the context of medicine, primary research may include real-life observations of disease presentations and outcomes. Single case descriptions, which often lead to new ideas and hypotheses, serve as important starting points or justifications for case series and cohort studies. The importance of case descriptions is particularly evident in the context of the COVID-19 pandemic when unique, educational case reports have heralded a new era in clinical medicine. 5

Case series serve similar purpose to single case reports, but are based on a slightly larger quantum of information. Observational studies, including online surveys, describe the existing phenomena at a larger scale, often involving various control groups. Observational studies include variable-scale epidemiological investigations at different time points. Interventional studies detail the results of therapeutic interventions.

Secondary research is based on already published literature and does not directly involve human or animal subjects. Review articles are generated by secondary research. These could be systematic reviews which follow methods akin to primary research but with the unit of study being published papers rather than humans or animals. Systematic reviews have a rigid structure with a mandatory search strategy encompassing multiple databases, systematic screening of search results against pre-defined inclusion and exclusion criteria, critical appraisal of study quality and an optional component of collating results across studies quantitatively to derive summary estimates (meta-analysis). 6 Narrative reviews, on the other hand, have a more flexible structure. Systematic literature searches to minimise bias in selection of articles are highly recommended but not mandatory. 7 Narrative reviews are influenced by the authors' viewpoint who may preferentially analyse selected sets of articles. 8

In relation to primary research, case studies and case series are generally not driven by a working hypothesis. Rather, they serve as a basis to generate a hypothesis. Observational or interventional studies should have a hypothesis for choosing research design and sample size. The results of observational and interventional studies further lead to the generation of new hypotheses, testing of which forms the basis of future studies. Review articles, on the other hand, may not be hypothesis-driven, but form fertile ground to generate future hypotheses for evaluation. Fig. 1 summarizes which type of studies are hypothesis-driven and which lead on to hypothesis generation.

STANDARDS OF WORKING AND SCIENTIFIC HYPOTHESES

A review of the published literature did not enable the identification of clearly defined standards for working and scientific hypotheses. It is essential to distinguish influential versus not influential hypotheses, evidence-based hypotheses versus a priori statements and ideas, ethical versus unethical, or potentially harmful ideas. The following points are proposed for consideration while generating working and scientific hypotheses. 1 , 2 Table 1 summarizes these points.

Evidence-based data

A scientific hypothesis should have a sound basis on previously published literature as well as the scientist's observations. Randomly generated (a priori) hypotheses are unlikely to be proven. A thorough literature search should form the basis of a hypothesis based on published evidence. 7

Unless a scientific hypothesis can be tested, it can neither be proven nor be disproven. Therefore, a scientific hypothesis should be amenable to testing with the available technologies and the present understanding of science.

Supported by pilot studies

If a hypothesis is based purely on a novel observation by the scientist in question, it should be grounded on some preliminary studies to support it. For example, if a drug that targets a specific cell population is hypothesized to be useful in a particular disease setting, then there must be some preliminary evidence that the specific cell population plays a role in driving that disease process.

Testable by ethical studies

The hypothesis should be testable by experiments that are ethically acceptable. 9 For example, a hypothesis that parachutes reduce mortality from falls from an airplane cannot be tested using a randomized controlled trial. 10 This is because it is obvious that all those jumping from a flying plane without a parachute would likely die. Similarly, the hypothesis that smoking tobacco causes lung cancer cannot be tested by a clinical trial that makes people take up smoking (since there is considerable evidence for the health hazards associated with smoking). Instead, long-term observational studies comparing outcomes in those who smoke and those who do not, as was performed in the landmark epidemiological case control study by Doll and Hill, 11 are more ethical and practical.

Balance between scientific temper and controversy

Novel findings, including novel hypotheses, particularly those that challenge established norms, are bound to face resistance for their wider acceptance. Such resistance is inevitable until the time such findings are proven with appropriate scientific rigor. However, hypotheses that generate controversy are generally unwelcome. For example, at the time the pandemic of human immunodeficiency virus (HIV) and AIDS was taking foot, there were numerous deniers that refused to believe that HIV caused AIDS. 12 , 13 Similarly, at a time when climate change is causing catastrophic changes to weather patterns worldwide, denial that climate change is occurring and consequent attempts to block climate change are certainly unwelcome. 14 The denialism and misinformation during the COVID-19 pandemic, including unfortunate examples of vaccine hesitancy, are more recent examples of controversial hypotheses not backed by science. 15 , 16 An example of a controversial hypothesis that was a revolutionary scientific breakthrough was the hypothesis put forth by Warren and Marshall that Helicobacter pylori causes peptic ulcers. Initially, the hypothesis that a microorganism could cause gastritis and gastric ulcers faced immense resistance. When the scientists that proposed the hypothesis themselves ingested H. pylori to induce gastritis in themselves, only then could they convince the wider world about their hypothesis. Such was the impact of the hypothesis was that Barry Marshall and Robin Warren were awarded the Nobel Prize in Physiology or Medicine in 2005 for this discovery. 17 , 18

DISTINGUISHING THE MOST INFLUENTIAL HYPOTHESES

Influential hypotheses are those that have stood the test of time. An archetype of an influential hypothesis is that proposed by Edward Jenner in the eighteenth century that cowpox infection protects against smallpox. While this observation had been reported for nearly a century before this time, it had not been suitably tested and publicised until Jenner conducted his experiments on a young boy by demonstrating protection against smallpox after inoculation with cowpox. 19 These experiments were the basis for widespread smallpox immunization strategies worldwide in the 20th century which resulted in the elimination of smallpox as a human disease today. 20

Other influential hypotheses are those which have been read and cited widely. An example of this is the hygiene hypothesis proposing an inverse relationship between infections in early life and allergies or autoimmunity in adulthood. An analysis reported that this hypothesis had been cited more than 3,000 times on Scopus. 1

LESSONS LEARNED FROM HYPOTHESES AMIDST THE COVID-19 PANDEMIC

The COVID-19 pandemic devastated the world like no other in recent memory. During this period, various hypotheses emerged, understandably so considering the public health emergency situation with innumerable deaths and suffering for humanity. Within weeks of the first reports of COVID-19, aberrant immune system activation was identified as a key driver of organ dysfunction and mortality in this disease. 21 Consequently, numerous drugs that suppress the immune system or abrogate the activation of the immune system were hypothesized to have a role in COVID-19. 22 One of the earliest drugs hypothesized to have a benefit was hydroxychloroquine. Hydroxychloroquine was proposed to interfere with Toll-like receptor activation and consequently ameliorate the aberrant immune system activation leading to pathology in COVID-19. 22 The drug was also hypothesized to have a prophylactic role in preventing infection or disease severity in COVID-19. It was also touted as a wonder drug for the disease by many prominent international figures. However, later studies which were well-designed randomized controlled trials failed to demonstrate any benefit of hydroxychloroquine in COVID-19. 23 , 24 , 25 , 26 Subsequently, azithromycin 27 , 28 and ivermectin 29 were hypothesized as potential therapies for COVID-19, but were not supported by evidence from randomized controlled trials. The role of vitamin D in preventing disease severity was also proposed, but has not been proven definitively until now. 30 , 31 On the other hand, randomized controlled trials identified the evidence supporting dexamethasone 32 and interleukin-6 pathway blockade with tocilizumab as effective therapies for COVID-19 in specific situations such as at the onset of hypoxia. 33 , 34 Clues towards the apparent effectiveness of various drugs against severe acute respiratory syndrome coronavirus 2 in vitro but their ineffectiveness in vivo have recently been identified. Many of these drugs are weak, lipophilic bases and some others induce phospholipidosis which results in apparent in vitro effectiveness due to non-specific off-target effects that are not replicated inside living systems. 35 , 36

Another hypothesis proposed was the association of the routine policy of vaccination with Bacillus Calmette-Guerin (BCG) with lower deaths due to COVID-19. This hypothesis emerged in the middle of 2020 when COVID-19 was still taking foot in many parts of the world. 37 , 38 Subsequently, many countries which had lower deaths at that time point went on to have higher numbers of mortality, comparable to other areas of the world. Furthermore, the hypothesis that BCG vaccination reduced COVID-19 mortality was a classic example of ecological fallacy. Associations between population level events (ecological studies; in this case, BCG vaccination and COVID-19 mortality) cannot be directly extrapolated to the individual level. Furthermore, such associations cannot per se be attributed as causal in nature, and can only serve to generate hypotheses that need to be tested at the individual level. 39

IS TRADITIONAL PEER REVIEW EFFICIENT FOR EVALUATION OF WORKING AND SCIENTIFIC HYPOTHESES?

Traditionally, publication after peer review has been considered the gold standard before any new idea finds acceptability amongst the scientific community. Getting a work (including a working or scientific hypothesis) reviewed by experts in the field before experiments are conducted to prove or disprove it helps to refine the idea further as well as improve the experiments planned to test the hypothesis. 40 A route towards this has been the emergence of journals dedicated to publishing hypotheses such as the Central Asian Journal of Medical Hypotheses and Ethics. 41 Another means of publishing hypotheses is through registered research protocols detailing the background, hypothesis, and methodology of a particular study. If such protocols are published after peer review, then the journal commits to publishing the completed study irrespective of whether the study hypothesis is proven or disproven. 42 In the post-pandemic world, online research methods such as online surveys powered via social media channels such as Twitter and Instagram might serve as critical tools to generate as well as to preliminarily test the appropriateness of hypotheses for further evaluation. 43 , 44

Some radical hypotheses might be difficult to publish after traditional peer review. These hypotheses might only be acceptable by the scientific community after they are tested in research studies. Preprints might be a way to disseminate such controversial and ground-breaking hypotheses. 45 However, scientists might prefer to keep their hypotheses confidential for the fear of plagiarism of ideas, avoiding online posting and publishing until they have tested the hypotheses.

SUGGESTIONS ON GENERATING AND PUBLISHING HYPOTHESES

Publication of hypotheses is important, however, a balance is required between scientific temper and controversy. Journal editors and reviewers might keep in mind these specific points, summarized in Table 2 and detailed hereafter, while judging the merit of hypotheses for publication. Keeping in mind the ethical principle of primum non nocere, a hypothesis should be published only if it is testable in a manner that is ethically appropriate. 46 Such hypotheses should be grounded in reality and lend themselves to further testing to either prove or disprove them. It must be considered that subsequent experiments to prove or disprove a hypothesis have an equal chance of failing or succeeding, akin to tossing a coin. A pre-conceived belief that a hypothesis is unlikely to be proven correct should not form the basis of rejection of such a hypothesis for publication. In this context, hypotheses generated after a thorough literature search to identify knowledge gaps or based on concrete clinical observations on a considerable number of patients (as opposed to random observations on a few patients) are more likely to be acceptable for publication by peer-reviewed journals. Also, hypotheses should be considered for publication or rejection based on their implications for science at large rather than whether the subsequent experiments to test them end up with results in favour of or against the original hypothesis.

Hypotheses form an important part of the scientific literature. The COVID-19 pandemic has reiterated the importance and relevance of hypotheses for dealing with public health emergencies and highlighted the need for evidence-based and ethical hypotheses. A good hypothesis is testable in a relevant study design, backed by preliminary evidence, and has positive ethical and clinical implications. General medical journals might consider publishing hypotheses as a specific article type to enable more rapid advancement of science.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Data curation: Gasparyan AY, Misra DP, Zimba O, Yessirkepov M, Agarwal V, Kitas GD.

Six Steps of the Scientific Method

Learn What Makes Each Stage Important

ThoughtCo. / Hugo Lin 

• Scientific Method
• Chemical Laws
• Periodic Table
• Projects & Experiments
• Biochemistry
• Physical Chemistry
• Medical Chemistry
• Chemistry In Everyday Life
• Famous Chemists
• Activities for Kids
• Abbreviations & Acronyms
• Weather & Climate
• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

The scientific method is a systematic way of learning about the world around us and answering questions. The key difference between the scientific method and other ways of acquiring knowledge are forming a hypothesis and then testing it with an experiment.

The Six Steps

The number of steps can vary from one description to another (which mainly happens when data and analysis are separated into separate steps), however, this is a fairly standard list of the six scientific method steps that you are expected to know for any science class:

• Purpose/Question Ask a question.
• Research Conduct background research. Write down your sources so you can cite your references. In the modern era, a lot of your research may be conducted online. Scroll to the bottom of articles to check the references. Even if you can't access the full text of a published article, you can usually view the abstract to see the summary of other experiments. Interview experts on a topic. The more you know about a subject, the easier it will be to conduct your investigation.
• Hypothesis Propose a hypothesis . This is a sort of educated guess about what you expect. It is a statement used to predict the outcome of an experiment. Usually, a hypothesis is written in terms of cause and effect. Alternatively, it may describe the relationship between two phenomena. One type of hypothesis is the null hypothesis or the no-difference hypothesis. This is an easy type of hypothesis to test because it assumes changing a variable will have no effect on the outcome. In reality, you probably expect a change but rejecting a hypothesis may be more useful than accepting one.
• Experiment Design and perform an experiment to test your hypothesis. An experiment has an independent and dependent variable. You change or control the independent variable and record the effect it has on the dependent variable . It's important to change only one variable for an experiment rather than try to combine the effects of variables in an experiment. For example, if you want to test the effects of light intensity and fertilizer concentration on the growth rate of a plant, you're really looking at two separate experiments.
• Data/Analysis Record observations and analyze the meaning of the data. Often, you'll prepare a table or graph of the data. Don't throw out data points you think are bad or that don't support your predictions. Some of the most incredible discoveries in science were made because the data looked wrong! Once you have the data, you may need to perform a mathematical analysis to support or refute your hypothesis.
• Conclusion Conclude whether to accept or reject your hypothesis. There is no right or wrong outcome to an experiment, so either result is fine. Accepting a hypothesis does not necessarily mean it's correct! Sometimes repeating an experiment may give a different result. In other cases, a hypothesis may predict an outcome, yet you might draw an incorrect conclusion. Communicate your results. The results may be compiled into a lab report or formally submitted as a paper. Whether you accept or reject the hypothesis, you likely learned something about the subject and may wish to revise the original hypothesis or form a new one for a future experiment.

When Are There Seven Steps?

Sometimes the scientific method is taught with seven steps instead of six. In this model, the first step of the scientific method is to make observations. Really, even if you don't make observations formally, you think about prior experiences with a subject in order to ask a question or solve a problem.

Formal observations are a type of brainstorming that can help you find an idea and form a hypothesis. Observe your subject and record everything about it. Include colors, timing, sounds, temperatures, changes, behavior, and anything that strikes you as interesting or significant.

When you design an experiment, you are controlling and measuring variables. There are three types of variables:

• Controlled Variables:  You can have as many  controlled variables  as you like. These are parts of the experiment that you try to keep constant throughout an experiment so that they won't interfere with your test. Writing down controlled variables is a good idea because it helps make your experiment  reproducible , which is important in science! If you have trouble duplicating results from one experiment to another, there may be a controlled variable that you missed.
• Independent Variable:  This is the variable you control.
• Dependent Variable:  This is the variable you measure. It is called the dependent variable because it  depends  on the independent variable.
• Examples of Independent and Dependent Variables
• Null Hypothesis Examples
• Difference Between Independent and Dependent Variables
• Scientific Method Flow Chart
• What Is an Experiment? Definition and Design
• How To Design a Science Fair Experiment
• What Is a Hypothesis? (Science)
• Scientific Variable
• What Are the Elements of a Good Hypothesis?
• Scientific Method Vocabulary Terms
• Understanding Simple vs Controlled Experiments
• What Is a Variable in Science?
• Null Hypothesis Definition and Examples
• Independent Variable Definition and Examples
• Scientific Method Lesson Plan

How to Develop a Good Research Hypothesis

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

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

Table of Contents

What is Hypothesis?

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

What is a Research Hypothesis?

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

Characteristics of a Good Research Hypothesis

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

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

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

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

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

Source: Educational Hub

How to formulate a research hypothesis.

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

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

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

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

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

3. Define the variables

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

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

4. Scrutinize the hypothesis

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

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

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

2. Complex Hypothesis

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

3. Directional Hypothesis

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

4. Non-directional Hypothesis

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

5. Associative and Causal Hypothesis

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

6. Null Hypothesis

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

7. Alternative Hypothesis

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

Research Hypothesis Examples of Independent and Dependent Variables

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

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

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

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

Importance of a Testable Hypothesis

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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

Thanks a lot for your valuable guidance.

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

Useful piece!

This is awesome.Wow.

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

Nicely explained

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

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

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

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

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

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Reporting Research

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

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

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

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

• Industry News

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

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

• Career Corner

Unlocking the Power of Networking in Academic Conferences

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

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

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

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

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

How to Design Effective Research Questionnaires for Robust Findings

Sign-up to read more

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

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

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

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

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

If you're seeing this message, it means we're having trouble loading external resources on our website.

If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.

To log in and use all the features of Khan Academy, please enable JavaScript in your browser.

Biology library

Course: biology library   >   unit 1.

• The scientific method
• Controlled experiments

The scientific method and experimental design

• (Choice A)   The facts collected from an experiment are written in the form of a hypothesis. A The facts collected from an experiment are written in the form of a hypothesis.
• (Choice B)   A hypothesis is the correct answer to a scientific question. B A hypothesis is the correct answer to a scientific question.
• (Choice C)   A hypothesis is a possible, testable explanation for a scientific question. C A hypothesis is a possible, testable explanation for a scientific question.
• (Choice D)   A hypothesis is the process of making careful observations. D A hypothesis is the process of making careful observations.

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

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 08 May 2024

The importance of distinguishing climate science from climate activism

• Ulf Büntgen   ORCID: orcid.org/0000-0002-3821-0818 1 , 2 , 3  

npj Climate Action volume  3 , Article number:  36 ( 2024 ) Cite this article

29k Accesses

924 Altmetric

Metrics details

I am concerned by climate scientists becoming climate activists, because scholars should not have a priori interests in the outcome of their studies. Likewise, I am worried about activists who pretend to be scientists, as this can be a misleading form of instrumentalization.

Background and motivation

It comes as no surprise that the slow production of scientific knowledge by an ever-growing international and interdisciplinary community of climate change researchers is not feasible to track the accelerating pace of cultural, political and economic perceptions of, and actions to the many threats anthropogenic global warming is likely to pose on natural and societal systems at different spatiotemporal scales. Recognition of a decoupling between “normal” and “post-normal” science is not new 1 , with the latter often being described as a legitimation of the plurality of knowledge in policy debates that became a liberating insight for many 2 . Characteristic for the yet unfolding phenomenon is an intermingling of science and policy 3 , in which political decisions are believed to be without any alternative (because they are scientifically predefined) and large parts of the scientific community accept a subordinate role to society (because there is an apparent moral obligation) 4 .

Motivated by the continuous inability of an international agreement to reduce greenhouse gas emissions to tackle global warming, despite an alarming recent rise in surface temperatures and associated hydroclimatic extremes 5 , I argue that quasi-religious belief in, rather than the understanding of the complex causes and consequences of climate and environmental changes undermines academic principles. I recommend that climate science and climate activism should be separated conceptually and practically, and the latter should not be confused with science communication and public engagement.

Climate science and climate activism

While this Comment is not a critique of climate activism per se, I am foremost concerned by an increasing number of climate scientists becoming climate activists, because scholars should not have a priori interests in the outcome of their studies. Like in any academic case, the quest for objectivity must also account for all aspects of global climate change research. While I have no problem with scholars taking public positions on climate issues, I see potential conflicts when scholars use information selectively or over-attribute problems to anthropogenic warming, and thus politicise climate and environmental change. Without self-critique and a diversity of viewpoints, scientists will ultimately harm the credibility of their research and possibly cause a wider public, political and economic backlash.

Likewise, I am worried about activists who pretend to be scientists, as this can be a misleading form of instrumentalization. In fact, there is just a thin line between the use and misuse of scientific certainty and uncertainty, and there is evidence for strategic and selective communication of scientific information for climate action 6 . (Non-)specialist activists often adopt scientific arguments as a source of moral legitimation for their movements 6 , which can be radical and destructive rather than rational and constructive. Unrestricted faith in scientific knowledge is, however, problematic because science is neither entitled to absolute truth nor ethical authority 7 . The notion of science to be explanatory rather than exploratory is a naïve overestimation that can fuel the complex field of global climate change to become a dogmatic ersatz religion for the wider public. It is also utterly irrational if activists ask to “follow the science” if there is no single direction. Again, even a clear-cut case like anthropogenically-induced global climate change does not justify the deviation from long-lasting scientific standards, which have distinguished the academic world from socio-economic and political spheres.

The role of recent global warming

Moreover, I find it misleading when prominent organisations, such as the Intergovernmental Panel on Climate Change (IPCC) in its latest summary for policymakers 5 , tend to overstate scientific understanding of the rate of recent anthropogenic warming relative to the range of past natural temperature variability over 2000 and even 125,000 years 8 , 9 , 10 , 11 . The quality and quantity of available climate proxy records are merely too low to allow for a robust comparison of the observed annual temperature extremes in the 21st century against reconstructed long-term climate means of the Holocene and before. Like all science, climate science is tentative and fallible 7 . This universal caveat emphasises the need for more research to reliably contextualise anthropogenic warming and better understand the sensitivity of the Earth’s climate system at different spatiotemporal scales 12 . Along these lines, I agree that the IPCC would benefit from a stronger involvement in economic research 13 , 14 , and that its neutral reports should inform but not prescribe climate policy 3 , 15 .

Furthermore, I cannot exclude that the ongoing pseudo-scientific chase for record-breaking heatwaves and associated hydroclimatic extremes distracts from scientifically guided international achievements of important long-term goals to reduce greenhouse gas emissions and mitigate global warming 16 . It is therefore only a bitter irony that the partial failure of COP28 coincided with the warmest year on record 17 , 18 , 19 . The temporal overshoot of 2023 now challenges the Paris Agreement to keep global warming well below 2 °C 20 . The IPCC’s special report 21 on exactly this scientifically questionable climate target 20 can be understood as a useful example of science communication that fostered a wide range of climate action 22 . The unprecedented recent temperature rise that follows increasing greenhouse gas concentrations 23 and has been amplified by an ongoing El Niño event 24 is likely to continue in 2024. This unparallel warming, however, has the unpleasant potential to trigger a dangerous zeitgeist of resignation and disregard—If it happened once, why shouldn’t it happen twice?

A way forward

In essence, I suggest that an ever-growing commingling of climate science, climate activism, climate communication and climate policy, whereby scientific insights are adopted to promote pre-determined positions, not only creates confusion among politicians, stakeholders and the wider public, but also diminishes academic credibility. Blurring boundaries between science and activism has the potential to harm movements of environmentalism and climate protection, as well as the much-needed international consent for sustainable growth and a global energy transition. If unbound climate activism results in widespread panic or indifference, people may think that it is either too late for action or that action does not matter. This argument is not in disagreement with the idea that mass mobilisation as an effective social response to climate change is only possible if society is experiencing sustained levels of risk 25 . Nevertheless, I would argue that motivations are more helpful than restrictions, at least in the long run. My criticism of an uncontrolled amalgamation of climate scientists and climate activists should not be understood as a general critique of climate activism, for which there are many constructive ways 26 , especially when accepting that climate mitigation and adaptation are both desirable options, and that non-action can be an important part of activism.

In conclusion, and as a way forward, I recommend that a neutral science should remain unbiased and avoid any form of selection, over-attribution and reductionism that would reflect a type of activism. Policymakers should continue seeking and considering nuanced information from an increasingly complex media landscape of overlapping academic, economic and public interests. Advice from a diversity of researchers and institutions beyond the IPCC and other large-scale organisations that assess the state of knowledge in specific scientific fields should include critical investigations of clear-cut cases, such as anthropogenic climate change. A successful, international climate agenda, including both climate mitigation and adaptation, requires reliable reporting of detailed and trustworthy certainties and uncertainties, whereas any form of scientism and exaggeration will be counterproductive.

Funtowicz, S. O. & Ravetz, J. R. Three types of risk assessment: a methodological analysis. In Risk Analysis in the Private Sector (eds. Whipple, C. & Covello, V. T.) 831–848 (Plenum Press, New York, 1885).

Ravetz, J. & Funtowicz, S. Post-normal science—an insight now maturing. Futures 31 , 641–646 (1999).

Google Scholar  

Storch, H.von & Krauß, W. Die Klimafalle. Die gefährliche Nähe von Politik und Klimaforschung. Hanser: Munich. Raumforsch. Raumord. https://doi.org/10.1007/s13147-014-0273-z (2013).

von Storch, H. COVID-19 und menschgemachter Klimawandel als postnormale wissenschaftliche Objekte. Naturwiss. Rundsch. 74 , 132–136 (2021).

IPCC. Summary for policymakers. In Climate Change 2021: the Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Masson-Delmotte, V. P. et al.) 3–32 (Cambridge University Press, Cambridge, 2021).

Sofdorf, A. & Burgi, V. Listen to the science!”—the role of scientific knowledge for the Fridays for Future movement. Front. Comm. 7 , 983929 (2022).

Article   Google Scholar  

Popper, K. Natural selection and the emergence of mind. Dialectica 32 , 339–355 (1978).

Esper, J. & Büntgen, U. The future of paleoclimate. Clim. Res. 83 , 57–59 (2021).

Anchukaitis, K. J. & Smerdon, J. E. Progress and uncertainties in global and hemispheric temperature reconstructions of the common era. Quat. Sci. Rev. 286 , 107537 (2022).

Essell, H. et al. A frequency-optimised temperature record for the Holocene. Environ. Res. Lett. 18 , 114022 (2023).

Esper, J., Schulz, P. & Büntgen, U. Is recent warming exceeding the range of natural variability of the past 125,000 years. Atmosphere 15 , 405 (2024).

Sherwood, S. C. et al. An assessment of Earth’s climate sensitivity using multiple lines of evidence. Rev. Geophys. 58 , e2019RG000678 (2020).

Article   CAS   Google Scholar  

Noy, I. Economists are not engaged enough with the IPCC. Clim. Action 2 , 23 (2023).

William D. Nordhaus, W. D. (eds) The Climate Casino: Risk, Uncertainty, and Economics for a Warming World (Yale University Press, 2013).

Pryck, De, K & Hulme, M. (eds) A Critical Assessment of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2023).

Hansen, J. E. et al. Global warming in the pipeline. Oxford Open Clim. Change 3 , kgad008 (2023).

Editorial Hoping for better. Nat. Clim. Change 14 , 1 (2024).

Voosen, P. The hottest year was even hotter than expected. Science 383 , 134 (2024).

Copernicus. 2023 Global Climate highlights . https://climate.copernicus.eu/ (2024).

Knutti, R., Rogelj, J., Sedláček, J. & Fischer, E. M. A scientific critique of the two-degree climate change target. Nat. Geosci. 9 , 13–18 (2016).

IPCC. Summary for policymakers. In Global Warming of 1.5   °C. An IPCC Special Report on the Impacts of Global Warming of 1.5   °C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty (eds. Masson-Delmotte, V. et al.) 3–24 (Cambridge University Press, Cambridge, 2018).

Doran, R., Ogunbode, C. A., Böhm, G. & Gregersen, T. Exposure to and learning from the IPCC special report on 1.5 °C global warming, and public support for climate protests and mitigation policies. npj Clim. Action 2 , 11 (2024).

Friedlingstein, P. et al. Global carbon budget 2023. Earth Syst. Sci. Data 15 , 5301–5369 (2023).

van Oldenborgh, G. J. et al. Defining El Niño indices in a warming climate. Environ. Res. Lett. 16 , 044003 (2021).

Fisher, D. R. AnthroShift in a warming world. npj Clim. Action 1 , 9 (2022).

Joshi, N., Agrawal, S., Ambury, H. & Parida, D. Advancing neighbourhood climate action: opportunities, challenges and way ahead. npj Clim. Action 3 , 7 (2024).

Download references

Acknowledgements

This study was supported by the Centre for Interdisciplinary Research (ZiF), Bielefeld, Germany, the Czech Science Foundation (# 23-08049S; Hydro8), the ERC Advanced Grant (# 882727; Monostar) and the ERC Synergy Grant (# 101118880; Synergy-Plague).

Author information

Authors and affiliations.

Department of Geography, University of Cambridge, Cambridge, UK

Ulf Büntgen

Global Change Research Institute (CzechGlobe), Czech Academy of Sciences, Brno, Czech Republic

Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic

You can also search for this author in PubMed   Google Scholar

Contributions

U.B. conceived the study and wrote the manuscript.

Corresponding author

Correspondence to Ulf Büntgen .

Ethics declarations

Competing interests.

The author declares no competing interests.

Additional information

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Büntgen, U. The importance of distinguishing climate science from climate activism. npj Clim. Action 3 , 36 (2024). https://doi.org/10.1038/s44168-024-00126-0

Download citation

Received : 11 March 2024

Accepted : 13 April 2024

Published : 08 May 2024

DOI : https://doi.org/10.1038/s44168-024-00126-0

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

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

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

• svg]:stroke-accent-900">

How sweat and stamina helped make humans exceptional runners–and hunters

By Laura Baisas

Posted on May 13, 2024 11:00 AM EDT

3 minute read

Rock art from Tadrart Acacus in Libya. This rocky massif is home to thousands of cave paintings in very different styles, dating from 12,000 BCE to 100 CE. They reflect marked changes in the fauna and flora, and also the different ways of life of the populations that succeeded one another in this region of the Sahara. © Federica Leone/UNESCO

Endurance running is not just a hobby for modern marathoners or for posting on social media. A new look into the anthropological history of endurance running when hunting for game shows that it may be just as efficient as other more traditional hunting methods like foraging . The findings are described in a study published May 13 in the journal Nature Human Behaviour and supports an idea called the endurance pursuit hypothesis .

“People who frequently practice running will know that when practiced on a regular basis, running becomes relatively easy,” study co-authors and evolutionary anthropologists Eugène Morin of Trent University in Canada and Bruce Winterhalder from the University of California, Davis tell PopSci . “From this, it is easy to envision how it could have conferred a selective advantage in hunting.”

What is the endurance pursuit hypothesis?

Initially, some anthropologists believed that running long distances in pursuit of game would have been taxing to the human body and not worth the trouble. A more slow paced pursuit was believed to be the better method to both get food while preserving strength . 

Endurance pursuit hypothesis posits that the human ability to run long distances is an adaptation that began roughly two million years ago. 

[Related: Humans are natural runners—and this ancient gene mutation might have helped .]

“Unlike other mammals, including other primates such as chimpanzees, humans can sweat profusely and have lower limb muscles evolved for stamina rather than power,” Morin and  Winterhalder say. “If economical and successful, endurance pursuits of medium-to-large game in a tropical environment could have [been] selected for that unique combination of traits: sweating and stamina.”

However, there have been few reports of contemporary humans using these endurance pursuits. Running long distances is generally viewed as energetically costly, and thus not worth it.

A game-hunting advantage

In their new study, Morin and Winterhalder studied roughly 400 recorded instances dating from the early 1500s to the early 2000s to investigate the role of endurance running while hunting. These included some first-hand accounts of different nomadic groups including the Evenki people in Siberia, the Innu in Canada, the Mbuti in the Democratic Republic of Congo, the Pitjantjatjara in Australia, the Inuit in Alaska, and more. Some of these ethnographic sources revealed that hunters sometimes ran over 62 miles in one pursuit. 

In addition to reviewing these accounts, the team used mathematical modeling to look at the various scenarios that could play out over a pursuit . They varied the size of the prey, how quickly or slowly a human pursued the animals (walking or running), and the distance covered.

The models found that in the right context, faster-paced endurance pursuit can increase energy returns. The models revealed that the calorific gains of endurance running were comparable to other hunting methods.

[Related: Why are humans good at endurance running? The answer is murky .]

“We were able to show that running or a mix of running and walking can be efficient, and it was a global practice by foragers prior to the modern era,” Morin and Winterhalder say. “In short, endurance pursuits would have provided hominins with an evolutionary advantage while competing with carnivores for game.”

The study suggests this type of hunting strategy would have been available to Pleistocene hominins (2.6 million to 11,700 years ago) and may also have had a role in human evolution. However, Morin and Winterhalder stress that this work does not speak directly to human’s evolutionary past, since it relies on ethnographic accounts from recent history.

Cultural bias

The team was surprised by the number of cases of long endurance pursuits that they found in environments that ranged from the bitter cold Canadian tundra to the more humid mountains of Hawaii. They also found that the study highlights a cultural bias in anthropology. 

“Committed recreational joggers and runners aside, Westerners tend to see running as ‘arduous,’ ‘costly,’ ‘challenging,’ and so on,” Morin and Winterhalder say. “In contrast, our observational data highlights Native societies that have encouraged and valued running, often for men, women and children, and in races and festive celebrations, as well as hunting.”

Morin and Winterhalder are currently working on another paper on endurance pursuits with more detail that assesses what may have motivated hunters and what seasons saw more endurance hunting. They are also working with collaborators Rebecca Bliege Bird and Doug Bird from Penn State on a study that will examine the division of labor in hunting.

Latest in Evolution

Rare skin fossil sheds new light on dinosaur feathers rare skin fossil sheds new light on dinosaur feathers, expressive elephants use gestures and vocal cues to communicate expressive elephants use gestures and vocal cues to communicate.

Main Container

Prime Minister of Canada Justin Trudeau

Search form Mobile

• Seoul Statement of Intent toward International Cooperation on AI Safety Science

Subscribe to email updates

Search form

Main content.

This website is not compatible with Internet Explorer or older version of Microsoft Edge(version 78 and older).

For full functionality please use a supported browser .

• backgrounders
• Gathered at the AI Seoul Summit 2024 on 21st May 2024, and following on from the AI Safety Summit in Bletchley Park on 2nd November 2023 and acknowledging the Safety Testing Chair’s Statement of Session Outcomes from the Bletchley Leaders’ Session, world leaders representing Australia, Canada, the European Union, France, Germany, Italy, Japan, the Republic of Korea, the Republic of Singapore, the United Kingdom, and the United States of America affirm the importance of international coordination and collaboration, based in openness, transparency, and reciprocity, to advance the science of AI safety. We affirm that safety is a key element in furtherance of responsible AI innovation.
• We acknowledge the need for a reliable, interdisciplinary, and reproducible body of evidence to inform policy efforts related to AI safety. We recognize the role of scientific inquiry and the benefits of international coordination for the advancement of such inquiry, so that ultimately the benefits of AI development and deployment are shared equitably around the globe.
• We affirm our intention to leverage and promote common scientific understandings through assessments such as the International AI Safety Report, to guide and align our respective policies, where appropriate, and to enable safe, secure, and trustworthy AI innovation, in line with our governance frameworks.
• We express our shared intent to take steps toward fostering common international scientific understanding on aspects of AI safety, including by endeavoring to promote complementarity and interoperability in our technical methodologies and overall approaches.
• These steps may include taking advantage of existing initiatives; the mutual strengthening of research, testing, and guidance capacities; the sharing of information about models, including their capabilities, limitations, and risks as appropriate; the monitoring of AI harms and safety incidents; the exchange or joint creation of evaluations, data sets and associated criteria, where appropriate; the establishment of shared technical resources for purposes of advancing the science of AI safety; and the promotion of appropriate research security practices in the field.
• We intend to coordinate our efforts to maximize efficiency, define priorities, report progress, enhance our outputs’ scientific rigor and robustness, promote the development and adoption of international standards, and accelerate the advancement of evidence-based approaches to AI safety.
• We articulate our shared ambition to develop an international network among key partners to accelerate the advancement of the science of AI safety. We look forward to close future collaboration, dialogue, and partnership on these and related endeavors.

Related Product

• Seoul Declaration for Safe, Innovative and Inclusive AI by Participants Attending the Leaders’ Session of the Al Seoul Summit, 21st May 2024  (aka Leaders’ Declaration)

• The Student Experience
• Financial Aid
• Degree Finder
• Undergraduate Arts & Sciences
• Departments and Programs
• Research, Scholarship & Creativity
• Centers & Institutes
• Geisel School of Medicine
• Guarini School of Graduate & Advanced Studies
• Thayer School of Engineering
• Tuck School of Business

Campus Life

• Diversity & Inclusion
• Athletics & Recreation
• Student Groups & Activities
• Residential Life

Psychological and Brain Sciences

Department of psychological and brain sciences.

• [email protected] Contact & Department Info Mail
• Undergraduate
• Major & Minor FAQs
• Honors FAQs
• Major and Minor Checklists
• Transfer and AP Credit
• Introductory
• Intermediate
• Independent Research
• Course Syllabi (ID login required)
• Request Form
• Winter 2025
• Spring 2025
• Research Areas
• Coursework and Curriculum
• Mentoring Toolkit
• Current Graduate Students
• Admissions FAQs
• Recent Dissertations
• Recent Research Findings
• Undergraduate Research
• Post-Doctoral Opportunities
• Participate in Experiments
• 2021 Honors Student Thesis Presentations (ID Login Required)
• 2022 Honors Student Thesis Presentations (ID Login Required)
• 2023 Honors Student Thesis Presentations (ID login required)
• Suggestions
• For Current PBS Community (ID Login Required)
• News & Events

Search form

Deepasri prasad awarded national science foundation graduate research fellowship, posted on may 21, 2024 by lisa d. aubrey.

Second-year PBS graduate student Deepasri Prasad was awarded a National Science Foundation Graduate Research Fellowship. The NSF GRFP grant is in recognition of Deepasri's high achieving scientific potential, and her winning project proposal, "Understanding the behavioral and neural effects of multisensory context on scene memory." This five-year fellowship provides three years of financial support as Deepasri works on her PhD in Caroline Robertson's Perception, Memory, and Neurodiversity Lab.

Engineering professor honored for distinguished scientific research

Civil engineer directs center for smart, sustainable & resilient infrastructure.

In 2019, Munir Nazzal , Ph.D., joined the University of Cincinnati College of Engineering and Applied Science as a professor in the Department of Civil and Architectural Engineering and Construction Management .

At UC, he has received various accolades for his advancements in transportation research, including the UC George Rieveschl Jr. Award for Distinguished Scientific Research for 2024. 

Munir Nazzal, Civil Engineering Professor and Director of the Center for Smart, Sustainable, and Resilient Infrastructure. Photo/Andrew Higley/UC Marketing + Brand

In addition to teaching, Nazzal is director of UC's Center for Smart, Sustainable & Resilient Infrastructure .

He is co-author of more than 160 peer-reviewed publications supported by more than $14 million in research funding. His work focuses on novel methods and technologies to advance sustainable transportation-related infrastructure.

In 2023 the Association of State Highway and Transportation Officials selected his project for the High Value Research Supplemental Award. 

“Dr. Nazzal’s work has a tremendous impact,” said Richard Miller , professor and head of UC's Department of Civil and Architectural Engineering and Construction Management.

“His work on studying infrastructure materials has also resulted in better, higher quality paving materials which is saving money, providing longer service life and improving the infrastructure construction industry. This work has the potential to have a huge impact on the ability of state departments of transportation to manage infrastructure.”

Nazzal and his team focus their research on the use of connected and autonomous (self-driving) vehicles for road assessments, with preparation of infrastructure for next-generation transportation. Recently, Nazzal developed an artificial intelligence system that uses connected and autonomous vehicle sensor data to assess damage in infrastructure assets, such as potholes.

Nazzal is partnering with Honda, the infrastructure engineering firm Parsons Corp., the consulting firm i-Probe and the Ohio Department of Transportation on the first project in the world to use sensors in production-level vehicles to examine assets of transportation infrastructure. The results of this project will have major impacts in the transportation sector, aiming to reduce costs, improve public safety by reducing accidents, and facilitate the safe, widespread deployment of connected and autonomous vehicles. The project improves roads by sensing things like where pavement markers are needed, where intersections need to be refined, and if signs are missing or obscured by the environment. To additionally support his research, Nazzal is also partnered with the Jurgensen Co., a local infrastructure construction firm.

Munir Nazzal with his family after receiving the 2024 George Rieveschl Jr. Award for Distinguished Scientific Research. Photo/Joseph Fuqua II/UC

Alongside his acclaimed research work, Nazzal is a member of Ohio State Transportation Invocation Council and is an editor for ASCE Journal of Materials in Civil Engineering and Buildings Journal. He continues to teach as a professor of civil engineering, and has mentored 35 graduate students.

“Dr. Nazzal’s work is of the highest quality, and it has a tremendous impact beyond Cincinnati,” said UC College of Engineering and Applied Science Dean John Weidner.

• Next Lives Here

The University of Cincinnati is classified as a Research 1 institution by the Carnegie Commission and is ranked in the National Science Foundation's Top-35 public research universities. UC's graduate students and faculty investigate problems and innovate solutions with real-world impact.  Next Lives Here .

Featured image at top: Munir Nazzal presents his transporation research. Photo/Andrew Higley/UC Marketing + Brand

• Faculty Staff
• Office of Research
• College of Engineering and Applied Science
• Civil and Architectural Engineering and Construction Management

Related Stories

May 21, 2024

In 2019, Munir Nazzal joined the University of Cincinnati College of Engineering and Applied Science as a professor in the Department of Civil and Architectural Engineering and Construction Management. Throughout his time at UC, he has received various accolades for his advancements in transportation research, including the UC George Rieveschl Jr. Award for Distinguished Scientific Research for 2024.

How can your car make roads safer?

November 20, 2023

The University of Cincinnati will work with Honda Motor Co., infrastructure engineering firm Parsons Corp., consulting firm i-Probe and the Ohio Department of Transportation to demonstrate that new cars can help evaluate roads.

UC students take on net-zero building challenge

October 10, 2022

Students in architecture and engineering programs at the University of Cincinnati are learning about the latest materials and strategies to create more energy-efficient indoor spaces. They will take part in the Solar Decathlon to design the next generation of efficient buildings.

Early iterations of the AI applications we interact with most today were built on traditional machine learning models. These models rely on learning algorithms that are developed and maintained by data scientists. In other words, traditional machine learning models need human intervention to process new information and perform any new task that falls outside their initial training.

For example, Apple made Siri a feature of its iOS in 2011. This early version of Siri was trained to understand a set of highly specific statements and requests. Human intervention was required to expand Siri’s knowledge base and functionality.

However, AI capabilities have been evolving steadily since the breakthrough development of  artificial neural networks  in 2012, which allow machines to engage in reinforcement learning and simulate how the human brain processes information.

Unlike basic machine learning models, deep learning models allow AI applications to learn how to perform new tasks that need human intelligence, engage in new behaviors and make decisions without human intervention. As a result, deep learning has enabled task automation, content generation, predictive maintenance and other capabilities across  industries .

Due to deep learning and other advancements, the field of AI remains in a constant and fast-paced state of flux. Our collective understanding of realized AI and theoretical AI continues to shift, meaning AI categories and AI terminology may differ (and overlap) from one source to the next. However, the types of AI can be largely understood by examining two encompassing categories: AI capabilities and AI functionalities.

1. Artificial Narrow AI

Artificial Narrow Intelligence, also known as Weak AI (what we refer to as Narrow AI), is the only type of AI that exists today. Any other form of AI is theoretical. It can be trained to perform a single or narrow task, often far faster and better than a human mind can.

However, it can’t perform outside of its defined task. Instead, it targets a single subset of cognitive abilities and advances in that spectrum. Siri, Amazon’s Alexa and IBM Watson are examples of Narrow AI. Even OpenAI’s ChatGPT is considered a form of Narrow AI because it’s limited to the single task of text-based chat.

2. General AI

Artificial General Intelligence (AGI), also known as  Strong AI , is today nothing more than a theoretical concept. AGI can use previous learnings and skills to accomplish new tasks in a different context without the need for human beings to train the underlying models. This ability allows AGI to learn and perform any intellectual task that a human being can.

3. Super AI

Super AI is commonly referred to as artificial superintelligence and, like AGI, is strictly theoretical. If ever realized, Super AI would think, reason, learn, make judgements and possess cognitive abilities that surpass those of human beings.

The applications possessing Super AI capabilities will have evolved beyond the point of understanding human sentiments and experiences to feel emotions, have needs and possess beliefs and desires of their own.

Underneath Narrow AI, one of the three types based on capabilities, there are two functional AI categories:

1. Reactive Machine AI

Reactive machines are AI systems with no memory and are designed to perform a very specific task. Since they can’t recollect previous outcomes or decisions, they only work with presently available data. Reactive AI stems from statistical math and can analyze vast amounts of data to produce a seemingly intelligent output.

Examples of Reactive Machine AI  

• IBM Deep Blue: IBM’s chess-playing supercomputer AI beat chess grandmaster Garry Kasparov in the late 1990s by analyzing the pieces on the board and predicting the probable outcomes of each move.
• The Netflix Recommendation Engine: Netflix’s viewing recommendations are powered by models that process data sets collected from viewing history to provide customers with content they’re most likely to enjoy.

2. Limited Memory AI

Unlike Reactive Machine AI, this form of AI can recall past events and outcomes and monitor specific objects or situations over time. Limited Memory AI can use past- and present-moment data to decide on a course of action most likely to help achieve a desired outcome.

However, while Limited Memory AI can use past data for a specific amount of time, it can’t retain that data in a library of past experiences to use over a long-term period. As it’s trained on more data over time, Limited Memory AI can improve in performance.

Examples of Limited Memory AI  

• Generative AI: Generative AI tools such as ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to predict the next word, phrase or visual element within the content it’s generating.
• Virtual assistants and chatbots: Siri, Alexa, Google Assistant, Cortana and IBM Watson Assistant combine natural language processing (NLP) and Limited Memory AI to understand questions and requests, take appropriate actions and compose responses.
• Self-driving cars: Autonomous vehicles use Limited Memory AI to understand the world around them in real-time and make informed decisions on when to apply speed, brake, make a turn, etc.

3. Theory of Mind AI

Theory of Mind AI is a functional class of AI that falls underneath the General AI. Though an unrealized form of AI today, AI with Theory of Mind functionality would understand the thoughts and emotions of other entities. This understanding can affect how the AI interacts with those around them. In theory, this would allow the AI to simulate human-like relationships.

Because Theory of Mind AI could infer human motives and reasoning, it would personalize its interactions with individuals based on their unique emotional needs and intentions. Theory of Mind AI would also be able to understand and contextualize artwork and essays, which today’s generative AI tools are unable to do.

Emotion AI is a theory of mind AI currently in development. AI researchers hope it will have the ability to analyze voices, images and other kinds of data to recognize, simulate, monitor and respond appropriately to humans on an emotional level. To date, Emotion AI is unable to understand and respond to human feelings.  

4. Self-Aware AI

Self-Aware AI is a kind of functional AI class for applications that would possess super AI capabilities. Like theory of mind AI, Self-Aware AI is strictly theoretical. If ever achieved, it would have the ability to understand its own internal conditions and traits along with human emotions and thoughts. It would also have its own set of emotions, needs and beliefs.

Emotion AI is a Theory of Mind AI currently in development. Researchers hope it will have the ability to analyze voices, images and other kinds of data to recognize, simulate, monitor and respond appropriately to humans on an emotional level. To date, Emotion AI is unable to understand and respond to human feelings.

Computer vision

Narrow AI applications with  computer vision  can be trained to interpret and analyze the visual world. This allows intelligent machines to identify and classify objects within images and video footage.

Applications of computer vision include:

• Image recognition and classification
• Object detection
• Object tracking
• Facial recognition
• Content-based image retrieval

Computer vision is critical for use cases that involve AI machines interacting and traversing the physical world around them. Examples include self-driving cars and machines navigating warehouses and other environments.

Robots in industrial settings can use Narrow AI to perform routine, repetitive tasks that involve materials handling, assembly and quality inspections. In healthcare, robots equipped with Narrow AI can assist surgeons in monitoring vitals and detecting potential issues during procedures.

Agricultural machines can engage in autonomous pruning, moving, thinning, seeding and spraying. And smart home devices such as the iRobot Roomba can navigate a home’s interior using computer vision and use data stored in memory to understand its progress.

Expert systems

Expert systems equipped with Narrow AI capabilities can be trained on a corpus to emulate the human decision-making process and apply expertise to solve complex problems. These systems can evaluate vast amounts of data to uncover trends and patterns to make decisions. They can also help businesses predict future events and understand why past events occurred.

IBM has pioneered AI from the very beginning, contributing breakthrough after breakthrough to the field. IBM most recently released a big upgrade to its cloud-based, generative AI platform known as watsonx.  IBM watsonx.ai  brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the entire AI lifecycle. With watsonx.ai, data scientists can build, train and deploy machine learning models in a single collaborative studio environment.

Get email updates about AI advancements, strategies, how-tos, expert perspectives and more.

Explore watsonx.ai today

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.

• Alzheimer's disease & dementia
• Arthritis & Rheumatism
• Attention deficit disorders
• Autism spectrum disorders
• Biomedical technology
• Diseases, Conditions, Syndromes
• Endocrinology & Metabolism
• Gastroenterology
• Gerontology & Geriatrics
• Health informatics
• Inflammatory disorders
• Medical economics
• Medical research
• Medications
• Neuroscience
• Obstetrics & gynaecology
• Oncology & Cancer
• Ophthalmology
• Overweight & Obesity
• Parkinson's & Movement disorders
• Psychology & Psychiatry
• Radiology & Imaging
• Sleep disorders
• Sports medicine & Kinesiology
• Vaccination
• Breast cancer
• Cardiovascular disease
• Chronic obstructive pulmonary disease
• Colon cancer
• Coronary artery disease
• Heart attack
• Heart disease
• High blood pressure
• Kidney disease
• Lung cancer
• Multiple sclerosis
• Myocardial infarction
• Ovarian cancer
• Post traumatic stress disorder
• Rheumatoid arthritis
• Schizophrenia
• Skin cancer
• Type 2 diabetes
• Full List »

share this!

May 21, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

written by researcher(s)

Synced brains: Why being constantly tuned in to your child's every need isn't always ideal

by Pascal Vrticka, The Conversation

It's crucial for healthy child development that children can form secure attachment bonds with their parents. Decades of research identified one key ingredient for this process: the coordination of parents' and children's brains and behavior during social interactions.

Humans connect with each other by synchronizing in many ways. Called bio-behavioral synchrony, this involves imitation of gestures and the alignment of heartbeats and hormone secretion (like cortisol and oxytocin). Even brains can synchronize —with brain activity decreasing and increasing in the same areas at roughly the same time when we spend time with others.

My colleagues and I carried out research which showed that brain-to-brain synchrony between parent and child can be helpful for children's attachment, and tends to rise when a parent and child play, talk or solve problems together. Recently, however, we started wondering whether more synchrony is always better. Our recent study, published in Developmental Science , suggests it can sometimes be a sign of relationship difficulties.

Is current parenting advice up to date?

A lot of current parenting advice recommends parents to be constantly "in sync" with their kids. It tells parents to be physically close and attuned to their children and to anticipate and immediately respond to their every need.

The advice is building upon attachment theory and research, which show that higher parental sensitivity and reflective functioning are beneficial for child development and secure attachment formation.

Yet, despite its good intentions, this advice misses several important details. For example, research revealed that for about 50-70% of the time , parents and children are not "in sync." During these times, they may be doing separate activities, such as a child exploring something on their own or a parent working. They rather engage in a constant "social dance" comprising being attuned to each other, failing to do so and repairing this disconnect.

And it's this flow of connection, disconnection and reconnection that offers children an ideal mixture of parental support and moderate, useful stress that helps growing children's social brains .

Researchers also agree that there can be negative consequences to parents and children constantly being tuned in to each other. For example, it can increase stress on the relationship and raise the risk for insecure child attachment. That is especially true if it is associated with parents overstimulating their child or being too responsive to their child's every need .

For parent-child synchrony, there thus appears to be an " optimal midrange ." Or, in other words, more synchrony may not necessarily be better.

Brain-to-brain synchrony and attachment

Within a large international team of investigators from across Europe, my colleagues Trinh Nguyen, Melanie Kungl, Stefanie Hoehl, Lars White and I set out to investigate how exactly parent-child bio-behavioral synchrony is linked to attachment.

We invited parent-child pairs—140 parents and their 5-to-6-year-old kids—to our SoNeAt Lab where they solved tangram puzzles together.

We measured brain activity with functional near-infrared spectroscopy (fNIRS) "hyperscanning," for which parents and children were asked to wear caps linked up with optical sensors. We also recorded videos of their interactions so we could assess how much behavioral synchrony they demonstrated—how attuned and attentive they were to each other. And finally, we assessed parents' and children's type of attachment—known as attachment representations .

We previously discovered increased neural synchrony in mother-child and father-child pairs during different tasks. In mother-child pairs, neural synchrony was linked to taking turns in solving puzzles or conversations . And in father-child pairs , synchrony during puzzling was linked to dads being confident about and enjoying their role as fathers. But does that mean higher parent-child neural synchrony is always a measure of a good relationship?

In our new study, we actually observed that mothers who had an insecure, anxious or avoidant attachment type showed more neural synchrony with their children. Interestingly, mothers' attachment types were unrelated to how synced mothers and children were in terms of their behavior. We also found increased neural but decreased behavioral synchrony in father-child pairs (compared to mother-child pairs) independent of attachment.

Our findings suggest that higher neural synchrony may be the result of putting increased cognitive effort into the parent-child interaction. If mothers' attachment representations are insecure, it may be more difficult for mums and kids to coordinate and help each other during activities such as puzzle solving.

A similar explanation may apply to neural synchrony during father-child problem-solving. Dads are more familiar with active, rough-and-tumble play . Engaging in structured and cognitively demanding activities such as puzzles may therefore be more challenging and require more neural synchrony for father-child pairs.

Lessons to be learned

What do our new findings mean? Most importantly, parents should not feel that they must be "in sync" with their kids all the time and at all costs. High parent-child attunement can also reflect interaction difficulties and can often add up to parental burnout , further negatively impacting the parent-child relationship.

It is of course helpful if parents are emotionally available, skilled in reading their children's cues and promptly and sensitively respond to their needs. Especially when children are young. However, it suffices for parents to be " good enough "—to be available when children need them rather than "always on."

Children can also benefit from freedom and independence emotionally, socially and cognitively, especially as they get older.

What really counts is that the parent-child relationship functions well overall. That children can develop trust in their parents and that any mismatches, which naturally occur all the time, are successfully repaired. That's the true essence of attachment theory, which is often missed and misrepresented in parenting advice.

To better navigate the challenging parenting path, parents need access to trustworthy and up to date sources of information. Together with the UK Charity Babygro, we therefore published the free-of-charge Babygro Book for Parents that provides them with evidence-based knowledge on parenting and child development .

It is our hope that our book can empower parents so that they feel reassured and confident in their own parenting choices and can optimally support their children to grow and thrive.

Explore further

Feedback to editors

Regular fish oil supplement use might increase first-time heart disease and stroke risk

2 hours ago

Pedestrians may be twice as likely to be hit by electric/hybrid cars as petrol/diesel ones

Study finds jaboticaba peel reduces inflammation and controls blood sugar in people with metabolic syndrome

3 hours ago

Study reveals how extremely rare immune cells predict how well treatments work for recurrent hives

5 hours ago

Researchers find connection between PFAS exposure in men and the health of their offspring

Researchers develop new tool for better classification of inherited disease-causing variants

Specialized weight navigation program shows higher use of evidence-based treatments, more weight lost than usual care

Exercise bouts could improve efficacy of cancer drug

6 hours ago

Drug-like inhibitor shows promise in preventing flu

Scientists discover a novel way of activating muscle cells' natural defenses against cancer using magnetic pulses

Related stories.

More synchrony between parents and children not always better, says study

Apr 9, 2024

Synced brains: How to bond with your kids – according to neuroscience

Jan 15, 2021

Study finds 'technoference' no worse for parent-child interactions than non-digital distractions

21 hours ago

Parenting stress may affect mother and child ability to tune in to each other

Aug 29, 2019

How children's secure attachment sets the stage for positive well-being

Oct 20, 2023

What factors influence children's learning of fear?

Feb 16, 2023

Recommended for you

Heart rate synchrony predicts effective group decision-making, research suggests

9 hours ago

Study models how ketamine's molecular action leads to its effects on the brain

10 hours ago

Researchers suggest brain scans for babies reduce risk of stroke later in life

12 hours ago

Ultra-processed foods increase cardiometabolic risk in children, study finds

7 hours ago

Fluoride exposure during pregnancy linked to increased risk of childhood neurobehavioral problems, study finds

May 20, 2024

Study shows exercise spurs neuron growth and rewires the brain, helping mice forget traumatic and addictive memories

Let us know if there is a problem with our content.

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Medical Xpress in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

Diverse headgear in hoofed mammals evolved from common ancestor

Genomic study supports hypothesis that ruminant horns and antlers did not evolve independently.

From the small ossicones on a giraffe to the gigantic antlers of a male moose -- which can grow as wide as a car -- the headgear of ruminant hooved mammals is extremely diverse, and new research suggests that despite the physical differences, fundamental aspects of these bony adaptations likely evolved from a common ancestor. This finding is published today in the journal Communications Biology by researchers from the American Museum of Natural History and Baruch College and the CUNY Graduate Center.

"Horns and antlers are incredibly diverse structures, and scientists have long debated their evolutionary origins," said Zachary Calamari, an assistant professor at Baruch College and the CUNY Graduate Center and a research associate at the Museum. "This genomic research not only gets us closer to solving an evolutionary mystery, but also helps us better understand how bone forms in all mammals."

There are about 170 modern ruminant hoofed mammal species with headgear, and many more in the fossil record. The headgear we see today comes in four types -- antlers, horns, ossicones, and pronghorns -- and they are used in a variety of ways, including for defense, recognition of other members of the species, and mating. Until recently, scientists were unsure if these various bony headgear evolved independently in each ruminant group or from a shared common ancestor.

As a comparative biology Ph.D. student in the Museum's Richard Gilder Graduate School, Calamari began investigating this question using genomic and computer-based 3D shape analysis. Working with the Museum's Frick Curator of Fossil Mammals John Flynn, Calamari focused on sequencing transcriptomes, the genes expressed in a tissue at a specific time, for headgear. Their research supports the idea that all of the ruminant headgear forms evolved from a common ancestor as paired bony outgrowths from the animals' "forehead," the area near the frontal bones of the skull.

"Our results provide more evidence that horns form from the cranial neural crest, an embryonic cell layer that forms the face, rather than from the cells that form the bones on the sides and back of the head," Flynn said. "It is striking that these are the same cells that form antlers. And the distinctive patterns of gene expression in cattle horns and deer antlers, relative to other bone and skin tissue "controls," provide compelling evidence of shared origin of fundamental aspects of these spectacular bony structures in an ancient ancestor."

By comparing their newly sequenced cattle horn transcriptome to deer antler and pig skin transcriptomes, Calamari and Flynn confirmed for the first time with transcriptomes that family-specific differences in headgear likely evolved as elaborations on a general bony structure inherited from a common ancestor.

"In addition to gene expression patterns that support a single origin of horns and antlers, our results also show the regulation of gene expression patterns in these structures may differ from other bones," Calamari said. "These results help us understand the evolutionary history of horns and antlers and could suggest that differences in other ruminant cranial appendages, like ossicones and pronghorns, are also elaborations on a shared ancestral cranial appendage."

This study was funded in part by the Richard Gilder Graduate School and the National Science Foundation, grant nos. DGE-0966166 and DDIG DEB-1601299.

• Evolutionary Biology
• Earth & Climate
• Early Mammals
• Early Humans
• Evolutionary psychology
• Convergent evolution
• Alternative fuel vehicle
• Sex linkage

Story Source:

Materials provided by American Museum of Natural History . Note: Content may be edited for style and length.

Journal Reference :

• Zachary T. Calamari, John J. Flynn. Gene expression supports a single origin of horns and antlers in hoofed mammals . Communications Biology , 2024; 7 (1) DOI: 10.1038/s42003-024-06134-4

Cite This Page :

Explore More

• Stopping Flu Before It Takes Hold
• Cosmic Rays Illuminate the Past
• Star Suddenly Vanish from the Night Sky
• Dinosaur Feather Evolution
• Warming Climate: Flash Droughts Worldwide
• Record Low Antarctic Sea Ice: Climate Change
• Brain 'Assembloids' Mimic Blood-Brain Barrier
• 'Doomsday' Glacier: Catastrophic Melting
• Blueprints of Self-Assembly
• Meerkat Chit-Chat

Trending Topics

Strange & offbeat.