This is the Difference Between a Hypothesis and a Theory

What to Know A hypothesis is an assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. A theory is a principle formed to explain the things already shown in data. Because of the rigors of experiment and control, it is much more likely that a theory will be true than a hypothesis.

As anyone who has worked in a laboratory or out in the field can tell you, science is about process: that of observing, making inferences about those observations, and then performing tests to see if the truth value of those inferences holds up. The scientific method is designed to be a rigorous procedure for acquiring knowledge about the world around us.

hypothesis

In scientific reasoning, a hypothesis is constructed before any applicable research has been done. A theory, on the other hand, is supported by evidence: it's a principle formed as an attempt to explain things that have already been substantiated by data.

Toward that end, science employs a particular vocabulary for describing how ideas are proposed, tested, and supported or disproven. And that's where we see the difference between a hypothesis and a theory .

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

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

What is a Hypothesis?

A hypothesis is usually tentative, an assumption or suggestion made strictly for the objective of being tested.

When a character which has been lost in a breed, reappears after a great number of generations, the most probable hypothesis is, not that the offspring suddenly takes after an ancestor some hundred generations distant, but that in each successive generation there has been a tendency to reproduce the character in question, which at last, under unknown favourable conditions, gains an ascendancy. Charles Darwin, On the Origin of Species , 1859 According to one widely reported hypothesis , cell-phone transmissions were disrupting the bees' navigational abilities. (Few experts took the cell-phone conjecture seriously; as one scientist said to me, "If that were the case, Dave Hackenberg's hives would have been dead a long time ago.") Elizabeth Kolbert, The New Yorker , 6 Aug. 2007

What is a Theory?

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

It is evident, on our theory , that coasts merely fringed by reefs cannot have subsided to any perceptible amount; and therefore they must, since the growth of their corals, either have remained stationary or have been upheaved. Now, it is remarkable how generally it can be shown, by the presence of upraised organic remains, that the fringed islands have been elevated: and so far, this is indirect evidence in favour of our theory . Charles Darwin, The Voyage of the Beagle , 1839 An example of a fundamental principle in physics, first proposed by Galileo in 1632 and extended by Einstein in 1905, is the following: All observers traveling at constant velocity relative to one another, should witness identical laws of nature. From this principle, Einstein derived his theory of special relativity. Alan Lightman, Harper's , December 2011

Non-Scientific Use

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch (though theory is more common in this regard):

The theory of the teacher with all these immigrant kids was that if you spoke English loudly enough they would eventually understand. E. L. Doctorow, Loon Lake , 1979 Chicago is famous for asking questions for which there can be no boilerplate answers. Example: given the probability that the federal tax code, nondairy creamer, Dennis Rodman and the art of mime all came from outer space, name something else that has extraterrestrial origins and defend your hypothesis . John McCormick, Newsweek , 5 Apr. 1999 In his mind's eye, Miller saw his case suddenly taking form: Richard Bailey had Helen Brach killed because she was threatening to sue him over the horses she had purchased. It was, he realized, only a theory , but it was one he felt certain he could, in time, prove. Full of urgency, a man with a mission now that he had a hypothesis to guide him, he issued new orders to his troops: Find out everything you can about Richard Bailey and his crowd. Howard Blum, Vanity Fair , January 1995

And sometimes one term is used as a genus, or a means for defining the other:

Laplace's popular version of his astronomy, the Système du monde , was famous for introducing what came to be known as the nebular hypothesis , the theory that the solar system was formed by the condensation, through gradual cooling, of the gaseous atmosphere (the nebulae) surrounding the sun. Louis Menand, The Metaphysical Club , 2001 Researchers use this information to support the gateway drug theory — the hypothesis that using one intoxicating substance leads to future use of another. Jordy Byrd, The Pacific Northwest Inlander , 6 May 2015 Fox, the business and economics columnist for Time magazine, tells the story of the professors who enabled those abuses under the banner of the financial theory known as the efficient market hypothesis . Paul Krugman, The New York Times Book Review , 9 Aug. 2009

Incorrect Interpretations of "Theory"

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

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

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

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

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

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Hypothesis vs. Theory

A hypothesis is either a suggested explanation for an observable phenomenon, or a reasoned prediction of a possible causal correlation among multiple phenomena. In science , a theory is a tested, well-substantiated, unifying explanation for a set of verified, proven factors. A theory is always backed by evidence; a hypothesis is only a suggested possible outcome, and is testable and falsifiable.

Comparison chart

Examples of theory and hypothesis.

Theory: Einstein's theory of relativity is a theory because it has been tested and verified innumerable times, with results consistently verifying Einstein's conclusion. However, simply because Einstein's conclusion has become a theory does not mean testing of this theory has stopped; all science is ongoing. See also the Big Bang theory , germ theory , and climate change .

Hypothesis: One might think that a prisoner who learns a work skill while in prison will be less likely to commit a crime when released. This is a hypothesis, an "educated guess." The scientific method can be used to test this hypothesis, to either prove it is false or prove that it warrants further study. (Note: Simply because a hypothesis is not found to be false does not mean it is true all or even most of the time. If it is consistently true after considerable time and research, it may be on its way to becoming a theory.)

This video further explains the difference between a theory and a hypothesis:

Common Misconception

People often tend to say "theory" when what they're actually talking about is a hypothesis. For instance, "Migraines are caused by drinking coffee after 2 p.m. — well, it's just a theory, not a rule."

This is actually a logically reasoned proposal based on an observation — say 2 instances of drinking coffee after 2 p.m. caused a migraine — but even if this were true, the migraine could have actually been caused by some other factors.

Because this observation is merely a reasoned possibility, it is testable and can be falsified — which makes it a hypothesis, not a theory.

  • What is a Scientific Hypothesis? - LiveScience
  • Wikipedia:Scientific theory

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Comments: Hypothesis vs Theory

Anonymous comments (2).

October 11, 2013, 1:11pm "In science, a theory is a well-substantiated, unifying explanation for a set of verified, proven hypotheses." But there's no such thing as "proven hypotheses". Hypotheses can be tested/falsified, they can't be "proven". That's just not how science works. Logical deductions based on axioms can be proven, but not scientific hypotheses. On top of that I find it somewhat strange to claim that a theory doesn't have to be testable, if it's built up from hypotheses, which DO have to be testable... — 80.✗.✗.139
May 6, 2014, 11:45pm "Evolution is a theory, not a fact, regarding the origin of living things." this statement is poorly formed because it implies that a thing is a theory until it gets proven and then it is somehow promoted to fact. this is just a misunderstanding of what the words mean, and of how science progresses generally. to say that a theory is inherently dubious because "it isn't a fact" is pretty much a meaningless statement. no expression which qualified as a mere fact could do a very good job of explaining the complicated process by which species have arisen on Earth over the last billion years. in fact, if you claimed that you could come up with such a single fact, now THAT would be dubious! everything we observe in nature supports the theory of evolution, and nothing we observe contradicts it. when you can say this about a theory, it's a pretty fair bet that the theory is correct. — 71.✗.✗.151
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Difference Between Hypothesis and Theory

hypothesis vs theory

The term ‘hypothesis’ is often contrasted with the term theory which implies an idea, typically proven, which aims at explaining facts and events. Both hypothesis and theory are important components of developing an approach, but these are not same. There exist a fine line of difference between hypothesis and theory, discussed in this article, have a look.

Content: Hypothesis Vs Theory

Comparison chart, definition of hypothesis.

An unproven statement or a mere assumption to be proved or disproved, about a factor, on which the researcher is interested, is called a hypothesis. It is a tentative statement, which is concerned with the relationship between two or more phenomena, as specified by the theoretical framework. The hypothesis has to go through a test, to determine its validity.

In other words, the hypothesis is a predictive statement, which can be objectively verified and tested through scientific methods, and relates the independent factor to the dependent one. To a researcher, a hypothesis is more like a question which he intends to resolve. The salient features of hypothesis are:

  • It must be clear and precise or else the reliability of the inferences drawn will be questioned.
  • It can be put to the test.
  • If the hypothesis is relational, it should state the relationship between independent and dependent variables.
  • The hypothesis should be open and responsive to testing within the stipulated time.
  • It should be limited in scope and must be clearly defined.

Definition of Theory

An idea or a broad range of ideas that are assumed to be true, which aims at explaining cause and effect relationship between multiple observed phenomena. It is based on hypothesis, which after a thorough analysis and continuous testing and confirmation through observation and experiments, becomes a theory. As it is backed by evidence, it is scientifically proven.

Just like hypothesis, theories can also be accepted or rejected. As more and more information is gathered on the subject, theories are modified accordingly, to increase the accuracy of prediction over time.

Key Differences Between Hypothesis and Theory

The points given below are vital, so far as the difference between hypothesis and theory is concerned:

  • Hypothesis refers to a supposition, based on few pieces of evidence, as an inception of further research or investigation. A theory is a well-affirmed explanation of natural phenomena, which is frequently validated through experimentation and observation.
  • While the hypothesis is based on a little amount of data, the theory is based on a wide set of data.
  • The hypothesis is an unproven statement; that can be tested. On the other hand, the theory is a scientifically tested and proven explanation of fact or event.
  • Hypothesis relies on suggestions, prediction, possibility or projects whereas a theory is supported by evidence and is verified.
  • The hypothesis may or may not be proved true, so the result is uncertain. On the contrary, the theory is one, that is assumed to be true and so its result is certain.
  • Hypothesis and theory are two levels of the scientific method, i.e. theory follows hypothesis and the basis for research is hypothesis whose outcome is a theory.

Both hypothesis and theory are testable and falsifiable. When a hypothesis is proved true, by passing all critical tests and analysis, it becomes a theory. So, the hypothesis is very different from theory, as the former is something unproven but the latter is a proven and tested statement.

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difference between hypothesis and theories

BELLENS MOTEBEJANE says

July 15, 2019 at 2:31 pm

AMAIZING !WHAT ARE THE DIFFERENCE BETWEEN THEORY AND LAW?

February 17, 2022 at 3:47 am

Thanks, I’m finally clear on this for the first time in my life of 65 years

Curtis Le Gendre says

September 14, 2022 at 8:02 am

Great Information

Kenneth says

November 19, 2022 at 2:10 am

I was looking for some takes on this topic, and I found your article quite informative. It has given me a fresh perspective on the topic tackled. Thanks!

Stefanie Banis says

February 9, 2024 at 6:35 pm

Very informative! Thank you! I understand the difference much better now!

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Scientific Theory Definition and Examples

Scientific Theory Definition

A scientific theory is a well-established explanation of some aspect of the natural world. Theories come from scientific data and multiple experiments. While it is not possible to prove a theory, a single contrary result using the scientific method can disprove it. In other words, a theory is testable and falsifiable.

Examples of Scientific Theories

There are many scientific theory in different disciplines:

  • Astronomy : theory of stellar nucleosynthesis , theory of stellar evolution
  • Biology : cell theory, theory of evolution, germ theory, dual inheritance theory
  • Chemistry : atomic theory, Bronsted Lowry acid-base theory , kinetic molecular theory of gases , Lewis acid-base theory , molecular theory, valence bond theory
  • Geology : climate change theory, plate tectonics theory
  • Physics : Big Bang theory, perturbation theory, theory of relativity, quantum field theory

Criteria for a Theory

In order for an explanation of the natural world to be a theory, it meets certain criteria:

  • A theory is falsifiable. At some point, a theory withstands testing and experimentation using the scientific method.
  • A theory is supported by lots of independent evidence.
  • A theory explains existing experimental results and predicts outcomes of new experiments at least as well as other theories.

Difference Between a Scientific Theory and Theory

Usually, a scientific theory is just called a theory. However, a theory in science means something different from the way most people use the word. For example, if frogs rain down from the sky, a person might observe the frogs and say, “I have a theory about why that happened.” While that theory might be an explanation, it is not based on multiple observations and experiments. It might not be testable and falsifiable. It’s not a scientific theory (although it could eventually become one).

Value of Disproven Theories

Even though some theories are incorrect, they often retain value.

For example, Arrhenius acid-base theory does not explain the behavior of chemicals lacking hydrogen that behave as acids. The Bronsted Lowry and Lewis theories do a better job of explaining this behavior. Yet, the Arrhenius theory predicts the behavior of most acids and is easier for people to understand.

Another example is the theory of Newtonian mechanics. The theory of relativity is much more inclusive than Newtonian mechanics, which breaks down in certain frames of reference or at speeds close to the speed of light . But, Newtonian mechanics is much simpler to understand and its equations apply to everyday behavior.

Difference Between a Scientific Theory and a Scientific Law

The scientific method leads to the formulation of both scientific theories and laws . Both theories and laws are falsifiable. Both theories and laws help with making predictions about the natural world. However, there is a key difference.

A theory explains why or how something works, while a law describes what happens without explaining it. Often, you see laws written in the form of equations or formulas.

Theories and laws are related, but theories never become laws or vice versa.

Theory vs Hypothesis

A hypothesis is a proposition that is tested via an experiment. A theory results from many, many tested hypotheses.

Theory vs Fact

Theories depend on facts, but the two words mean different things. A fact is an irrefutable piece of evidence or data. Facts never change. A theory, on the other hand, may be modified or disproven.

Difference Between a Theory and a Model

Both theories and models allow a scientist to form a hypothesis and make predictions about future outcomes. However, a theory both describes and explains, while a model only describes. For example, a model of the solar system shows the arrangement of planets and asteroids in a plane around the Sun, but it does not explain how or why they got into their positions.

  • Frigg, Roman (2006). “ Scientific Representation and the Semantic View of Theories .”  Theoria . 55 (2): 183–206. 
  • Halvorson, Hans (2012). “What Scientific Theories Could Not Be.”  Philosophy of Science . 79 (2): 183–206. doi: 10.1086/664745
  • McComas, William F. (December 30, 2013).  The Language of Science Education: An Expanded Glossary of Key Terms and Concepts in Science Teaching and Learning . Springer Science & Business Media. ISBN 978-94-6209-497-0.
  • National Academy of Sciences (US) (1999). Science and Creationism: A View from the National Academy of Sciences (2nd ed.). National Academies Press. doi: 10.17226/6024  ISBN 978-0-309-06406-4. 
  • Suppe, Frederick (1998). “Understanding Scientific Theories: An Assessment of Developments, 1969–1998.”  Philosophy of Science . 67: S102–S115. doi: 10.1086/392812

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Words have precise meanings in science. For example, "theory," "law," and "hypothesis" don't all mean the same thing. Outside of science, you might say something is "just a theory," meaning it's a supposition that may or may not be true. In science, however, a theory is an explanation that generally is accepted to be true. Here's a closer look at these important, commonly misused terms.

A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true.

Example: If you see no difference in the cleaning ability of various laundry detergents, you might hypothesize that cleaning effectiveness is not affected by which detergent you use. This hypothesis can be disproven if you observe a stain is removed by one detergent and not another. On the other hand, you cannot prove the hypothesis. Even if you never see a difference in the cleanliness of your clothes after trying 1,000 detergents, there might be one more you haven't tried that could be different.

Scientists often construct models to help explain complex concepts. These can be physical models like a model volcano or atom  or conceptual models like predictive weather algorithms. A model doesn't contain all the details of the real deal, but it should include observations known to be valid.

Example: The  Bohr model shows electrons orbiting the atomic nucleus, much the same way as the way planets revolve around the sun. In reality, the movement of electrons is complicated but the model makes it clear that protons and neutrons form a nucleus and electrons tend to move around outside the nucleus.

A scientific theory summarizes a hypothesis or group of hypotheses that have been supported with repeated testing. A theory is valid as long as there is no evidence to dispute it. Therefore, theories can be disproven. Basically, if evidence accumulates to support a hypothesis, then the hypothesis can become accepted as a good explanation of a phenomenon. One definition of a theory is to say that it's an accepted hypothesis.

Example: It is known that on June 30, 1908, in Tunguska, Siberia, there was an explosion equivalent to the detonation of about 15 million tons of TNT. Many hypotheses have been proposed for what caused the explosion. It was theorized that the explosion was caused by a natural extraterrestrial phenomenon , and was not caused by man. Is this theory a fact? No. The event is a recorded fact. Is this theory, generally accepted to be true, based on evidence to-date? Yes. Can this theory be shown to be false and be discarded? Yes.

A scientific law generalizes a body of observations. At the time it's made, no exceptions have been found to a law. Scientific laws explain things but they do not describe them. One way to tell a law and a theory apart is to ask if the description gives you the means to explain "why." The word "law" is used less and less in science, as many laws are only true under limited circumstances.

Example: Consider Newton's Law of Gravity . Newton could use this law to predict the behavior of a dropped object but he couldn't explain why it happened.

As you can see, there is no "proof" or absolute "truth" in science. The closest we get are facts, which are indisputable observations. Note, however, if you define proof as arriving at a logical conclusion, based on the evidence, then there is "proof" in science. Some work under the definition that to prove something implies it can never be wrong, which is different. If you're asked to define the terms hypothesis, theory, and law, keep in mind the definitions of proof and of these words can vary slightly depending on the scientific discipline. What's important is to realize they don't all mean the same thing and cannot be used interchangeably.

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Difference Between Hypothesis and Theory

Main difference – hypothesis vs theory.

Hypothesis and theory are two words that are often used in the field of science and research. Although these two words have somewhat similar meanings, there is a fundamental difference between hypothesis and theory. Hypothesis is a suggested explanation to explain some phenomenon, and is based on limited data. Theory, on the other hand, is a set of ideas that is intended to explain facts or events; they are based on concrete evidence. This is the main difference between hypothesis and theory.

This article explains,

1. What is a Hypothesis? – Definitions and Features

2. What is a Theory? – Definitions and Features

Difference Between Hypothesis and Theory - Hypothesis vs Theory Comparison Summary

What is a Hypothesis

A hypothesis is a proposed explanation based on some evidence.  According to the Oxford dictionary, hypothesis is “a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation” and Merriam-Webster dictionary defines it as “an idea or theory that is not proven but that leads to further study or discussion.”

However, a hypothesis is not scientifically tested or proven; it is a logical assumption based on the available evidence. A hypothesis can be accurate or inaccurate. Once the hypothesis is scientifically tested and proven, it becomes a theory.

Main Difference - Hypothesis vs Theory

The hypothesis of Andreas Cellarius, showing the planetary motions in eccentric and epicyclical orbits.

What is a Theory

Theory is an idea or set of ideas that is intended to explain facts or events. A theory is formulated after in-depth research analysis. It is always proven scientifically with evidence. The Oxford dictionary defines theory as “supposition or a system of ideas intended to explain something, especially one based on general principles independent of the thing to be explained.”

As mentioned above, a theory is usually formulated from a hypothesis. Once a hypothesis is tested and proven, it is accepted as a theory. Copernicus’ Heliocentric theory, Darwin’s theory of evolution, quantum theory, special relativity theory, are examples of are some important scientific theories.

A theory can be used to understand, explain and make predictions over a concept. However, theories can be proven to be wrong as well, depending on the proof. However, theoretical knowledge is important in understanding different concepts and situations.

Difference Between Hypothesis and Theory

Special Theory of Relativity

Definition 

Hypothesis is a proposed explanation for some phenomenon based on limited evidence.

Theory is an idea or set of ideas that is intended to explain facts or events.

Testing and Proof

Hypothesis is not scientifically tested or proven.

Theory is scientifically tested and proven.

Hypothesis is based on limited data.

Theory is based on a wide range of data.

Interdependence

Hypothesis can lead to a theory.

Theory can be formulated through a hypothesis.

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“Theory” vs. “Hypothesis”: What Is The Difference?

Chances are you’ve heard of the TV show The Big Bang Theory . Lots of people love this lighthearted sitcom for its quirky characters and their relationships, but others haven’t even given the series a chance for one reason: they don’t like science and assume the show is boring.

However, it only takes a few seconds with Sheldon and Penny to disprove this assumption and realize that this theory ab0ut The Big Bang Theory is wrong—it isn’t a scientific snoozefest.

But wait: is it a theory or a  hypothesis about the show that leads people astray? And would the actual big bang theory— the one that refers to the beginning of the universe—mean the same thing as a big bang hypothesis ?

Let’s take a closer look at theory and hypothesis to nail down what they mean.

What does theory mean?

As a noun, a theory is a group of tested general propositions “commonly regarded as correct, that can be used as principles of explanation and prediction for a class of phenomena .” This is what is known as a scientific   theory , which by definition is “an understanding that is based on already tested data or results .” Einstein’s theory of relativity and the  theory of evolution are both examples of such tested propositions .

Theory is also defined as a proposed explanation you might make about your own life and observations, and it’s one “whose status is still conjectural and subject to experimentation .” For example:  I’ve got my own theories about why he’s missing his deadlines all the time.  This example refers to an idea that has not yet been proven.

There are other uses of the word theory as well.

  • In this example,  theory is “a body of principles or theorems belonging to one subject.” It can be a branch of science or art that deals with its principles or methods .
  • For example: when she started to follow a new parenting theory based on a trendy book, it caused a conflict with her mother, who kept offering differing opinions .

First recorded in 1590–1600, theory originates from the Late Latin theōria , which stems from the Greek theōría. Synonyms for theory include approach , assumption , doctrine , ideology , method , philosophy , speculation , thesis , and understanding .

What does hypothesis mean?

Hypothesis is a noun that means “a proposition , or set of propositions, set forth as an explanation” that describe “some specified group of phenomena.” Sounds familiar to theory , no?

But, unlike a theory , a scientific  hypothesis is made before testing is done and isn’t based on results. Instead, it is the basis for further investigation . For example: her working hypothesis is that this new drug also has an unintended effect on the heart, and she is curious what the clinical trials  will show .

Hypothesis also refers to “a proposition assumed as a premise in an argument,” or “mere assumption or guess.” For example:

  • She decided to drink more water for a week to test out her hypothesis that dehydration was causing her terrible headaches.
  • After a night of her spouse’s maddening snoring, she came up with the hypothesis that sleeping on his back was exacerbating the problem.

Hypothesis was first recorded around 1590–1600 and originates from the Greek word hypóthesis (“basis, supposition”). Synonyms for hypothesis include: assumption , conclusion , conjecture , guess , inference , premise , theorem , and thesis .

How to use each

Although theory in terms of science is used to express something based on extensive research and experimentation, typically in everyday life, theory is used more casually to express an educated guess.

So in casual language,  theory and hypothesis are more likely to be used interchangeably to express an idea or speculation .

In most everyday uses, theory and hypothesis convey the same meaning. For example:

  • Her opinion is just a theory , of course. She’s just guessing.
  • Her opinion is just a hypothesis , of course. She’s just guessing.

It’s important to remember that a scientific   theory is different. It is based on tested results that support or substantiate it, whereas a hypothesis is formed before the research.

For example:

  • His  hypothesis  for the class science project is that this brand of plant food is better than the rest for helping grass grow.
  • After testing his hypothesis , he developed a new theory based on the experiment results: plant food B is actually more effective than plant food A in helping grass grow.

In these examples, theory “doesn’t mean a hunch or a guess,” according to Kenneth R. Miller, a cell biologist at Brown University. “A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

So if you have a concept that is based on substantiated research, it’s a theory .

But if you’re working off of an assumption that you still need to test, it’s a hypothesis .

So remember, first comes a hypothesis , then comes theory . Now who’s ready for a  Big Bang Theory marathon?

Now that you’ve theorized and hypothesized through this whole article … keep testing your judgment (Or is it judgement?). Find out the correct spelling here!

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1.2: Theories, Hypotheses and Models

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For the purpose of this textbook (and science in general), we introduce a distinction in what we mean by “theory”, “hypothesis”, and by “model”. We will consider a “theory” to be a set of statements (or an equation) that gives us a broad description, applicable to several phenomena and that allows us to make verifiable predictions. For example, Chloë’s Theory ( \(t \propto \sqrt{h}\) ) can be considered a theory. Specifically, we do not use the word theory in the context of “I have a theory about this...”

A “hypothesis” is a consequence of the theory that one can test. From Chloë’s Theory, we have the hypothesis that an object will take \(\sqrt{2}\) times longer to fall from \(1\:\text{m}\) than from \(2\:\text{m}\) . We can formulate the hypothesis based on the theory and then test that hypothesis. If the hypothesis is found to be invalidated by experiment, then either the theory is incorrect, or the hypothesis is not consistent with the theory.

A “model” is a situation-specific description of a phenomenon based on a theory , that allows us to make a specific prediction. Using the example from the previous section, our theory would be that the fall time of an object is proportional to the square root of the drop height, and a model would be applying that theory to describe a tennis ball falling by \(4.2\) m. From the model, we can form a testable hypothesis of how long it will take the tennis ball to fall that distance. It is important to note that a model will almost always be an approximation of the theory applied to describe a particular phenomenon. For example, if Chloë’s Theory is only valid in vacuum, and we use it to model the time that it take for an object to fall at the surface of the Earth, we may find that our model disagrees with experiment. We would not necessarily conclude that the theory is invalidated, if our model did not adequately apply the theory to describe the phenomenon (e.g. by forgetting to include the effect of air drag).

This textbook will introduce the theories from Classical Physics, which were mostly established and tested between the seventeenth and nineteenth centuries. We will take it as given that readers of this textbook are not likely to perform experiments that challenge those well-established theories. The main challenge will be, given a theory, to define a model that describes a particular situation, and then to test that model. This introductory physics course is thus focused on thinking of “doing physics” as the task of correctly modeling a situation.

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What’s the difference between a model and a theory?

“Model” and “Theory” are sometimes used interchangeably among scientists. In physics, it is particularly important to distinguish between these two terms. A model provides an immediate understanding of something based on a theory.

For example, if you would like to model the launch of your toy rocket into space, you might run a computer simulation of the launch based on various theories of propulsion that you have learned. In this case, the model is the computer simulation, which describes what will happen to the rocket. This model depends on various theories that have been extensively tested such as Newton’s Laws of motion, Fluid dynamics, etc.

  • “Model”: Your homemade rocket computer simulation
  • “Theory”: Newton’s Laws of motion, Fluid dynamics

With this analogy, we can quickly see that the “model” and “theory” are not interchangeable. If they were, we would be saying that all of Newton’s Laws of Motion depend on the success of your piddly toy rocket computer simulation!

Exercise \(\PageIndex{2}\)

Models cannot be scientifically tested, only theories can be tested.

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1.6: Hypothesis, Theories, and Laws

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  Learning Objectives

  • Describe the difference between hypothesis and theory as scientific terms.
  • Describe the difference between a theory and scientific law.

Although many have taken science classes throughout the course of their studies, people often have incorrect or misleading ideas about some of the most important and basic principles in science. Most students have heard of hypotheses, theories, and laws, but what do these terms really mean? Prior to reading this section, consider what you have learned about these terms before. What do these terms mean to you? What do you read that contradicts or supports what you thought?

What is a Fact?

A fact is a basic statement established by experiment or observation. All facts are true under the specific conditions of the observation.

What is a Hypothesis?

One of the most common terms used in science classes is a "hypothesis". The word can have many different definitions, depending on the context in which it is being used:

  • An educated guess: a scientific hypothesis provides a suggested solution based on evidence.
  • Prediction: if you have ever carried out a science experiment, you probably made this type of hypothesis when you predicted the outcome of your experiment.
  • Tentative or proposed explanation: hypotheses can be suggestions about why something is observed. In order for it to be scientific, however, a scientist must be able to test the explanation to see if it works and if it is able to correctly predict what will happen in a situation. For example, "if my hypothesis is correct, we should see ___ result when we perform ___ test."
A hypothesis is very tentative; it can be easily changed.

What is a Theory?

The United States National Academy of Sciences describes what a theory is as follows:

"Some scientific explanations are so well established that no new evidence is likely to alter them. The explanation becomes a scientific theory. In everyday language a theory means a hunch or speculation. Not so in science. In science, the word theory refers to a comprehensive explanation of an important feature of nature supported by facts gathered over time. Theories also allow scientists to make predictions about as yet unobserved phenomena."

"A scientific theory is a well-substantiated explanation of some aspect of the natural world, based on a body of facts that have been repeatedly confirmed through observation and experimentation. Such fact-supported theories are not "guesses" but reliable accounts of the real world. The theory of biological evolution is more than "just a theory." It is as factual an explanation of the universe as the atomic theory of matter (stating that everything is made of atoms) or the germ theory of disease (which states that many diseases are caused by germs). Our understanding of gravity is still a work in progress. But the phenomenon of gravity, like evolution, is an accepted fact.

Note some key features of theories that are important to understand from this description:

  • Theories are explanations of natural phenomena. They aren't predictions (although we may use theories to make predictions). They are explanations as to why we observe something.
  • Theories aren't likely to change. They have a large amount of support and are able to satisfactorily explain numerous observations. Theories can, indeed, be facts. Theories can change, but it is a long and difficult process. In order for a theory to change, there must be many observations or pieces of evidence that the theory cannot explain.
  • Theories are not guesses. The phrase "just a theory" has no room in science. To be a scientific theory carries a lot of weight; it is not just one person's idea about something
Theories aren't likely to change.

What is a Law?

Scientific laws are similar to scientific theories in that they are principles that can be used to predict the behavior of the natural world. Both scientific laws and scientific theories are typically well-supported by observations and/or experimental evidence. Usually scientific laws refer to rules for how nature will behave under certain conditions, frequently written as an equation. Scientific theories are more overarching explanations of how nature works and why it exhibits certain characteristics. As a comparison, theories explain why we observe what we do and laws describe what happens.

For example, around the year 1800, Jacques Charles and other scientists were working with gases to, among other reasons, improve the design of the hot air balloon. These scientists found, after many, many tests, that certain patterns existed in the observations on gas behavior. If the temperature of the gas is increased, the volume of the gas increased. This is known as a natural law. A law is a relationship that exists between variables in a group of data. Laws describe the patterns we see in large amounts of data, but do not describe why the patterns exist.

What is a Belief?

A belief is a statement that is not scientifically provable. Beliefs may or may not be incorrect; they just are outside the realm of science to explore.

Laws vs. Theories

A common misconception is that scientific theories are rudimentary ideas that will eventually graduate into scientific laws when enough data and evidence has accumulated. A theory does not change into a scientific law with the accumulation of new or better evidence. Remember, theories are explanations and laws are patterns we see in large amounts of data, frequently written as an equation. A theory will always remain a theory; a law will always remain a law.

Video \(\PageIndex{1}\): What’s the difference between a scientific law and theory?

  • A hypothesis is a tentative explanation that can be tested by further investigation.
  • A theory is a well-supported explanation of observations.
  • A scientific law is a statement that summarizes the relationship between variables.
  • An experiment is a controlled method of testing a hypothesis.

Contributions & Attributions

Marisa Alviar-Agnew  ( Sacramento City College )

Henry Agnew (UC Davis)

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  • Difference Between Hypothesis And Theory

Difference Between Theory and Hypothesis

Many of them belittle evolution because “it is just a theory.” Gravity, on the other hand, must be real because it is a law. The words “theory,” “facts,” “laws” and “hypothesis” have a very specific meaning in the scientific world that doesn’t quite match the ones we use in everyday language. A hypothesis is a tentative explanation of an observation that can be tested. It acts as a starting point for further explanation. Theory, on the other hand, is an explanation of some aspect of the natural world that’s well-justified by facts, tested hypotheses, and laws. Let us look at more differences between hypothesis and theory given in a tabular column below.

Theory vs Hypothesis

From the above differences, we can infer that a hypothesis might change significantly as the testing occurs. A hypothesis can either be right or wrong. When a hypothesis is tested and proved true, it becomes a theory. At BYJU’S, learn more differences like the difference between asteroid and comet.

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The Scientific Hypothesis

The Key to Understanding How Science Works

Hypotheses, Theories, Laws (and Models)… What’s the difference?

Untold hours have been spent trying to sort out the differences between these ideas. should we bother.

Ask what the differences between these concepts are and you’re likely to encounter a raft of distinctions; typically with charts and ladders of generality leading from hypotheses to theories and, ultimately, to laws.   Countless students have been exposed to and forced to learn how the schemes are set up.  Theories are said to be well-tested hypotheses, or maybe whole collections of linked hypotheses, and laws, well, laws are at the top of the heap, the apex of science having enormous reach, quantitative predictive power, and validity.  It all seems so clear.

Yet there are many problems with the general scheme.  For one thing, it is never quite explained how a hypothesis turns into a theory or law and, consequently, the boundaries are blurry, and definitions tend vary with the speaker.  And there is no consistency in usage across fields, I’ll give some examples in a minute.  There are branches of science that have few if any theories and no laws – neuroscience comes to mind – though no one doubts that neuroscience is a bona fide science that has discovered great quantities of reliable and useful information and wide-ranging generalizations.  At the other extreme, there are sciences that spin out theories at a dizzying pace – psychology, for instance – although the permanence and indeed the veracity of psychological theories are rarely on par with those of physics or chemistry.

Some people will tell you that theories and laws are “more quantitative” than hypotheses, but the most famous theory in biology, the Theory of Evolution, which is based on concepts such as heritability, genetic variability, natural selection, etc. is not as neatly expressible in quantitative terms as is Newton’s Theory of Gravity, for example.   And what do we make of the fact that Newton’s “Law of Gravity” was superceded by Einstein’s “General Theory (not Law) of Relativity?”

What about the idea that a hypothesis is a low-level explanation that somehow transmogrifies into a theory when conditions are right?  Even this simple rule is not adhered to.  Take geology (or “geoscience” nowadays):  We have the Alvarez Hypothesis about how an asteroid slamming into the earth caused the extinction of dinosaurs and other life-forms ~66 million years ago.  The Alvarez Hypothesis explains, often in quantitative detail, many important phenomena and makes far-reaching predictions, most remarkably of a crater, which was eventually found in the Yucatan peninsula, that has the right age and size to be the site of an extinction-causing asteroid impact.  The Alvarez Hypothesis has been rigorously tested many times since it was proposed, without having been promoted to a theory. 

But perhaps the Alvarez Hypothesis is still thought to be a tentative explanation, not yet worthy of a more exalted status? It seems that the same can’t be said about the idea that the earth’s crust consists of 12 or so rigid “plates” of solid material that drift around very slowly and create geological phenomena, such as mountain ranges and earth-quakes, when they crash into each other.  This is called either the “Plate Tectonics Hypothesis” or “Plate Tectonics Theory” by different authors.  Same data, same interpretations, same significance, different names. 

And for anyone trying to make sense of the hypothesis-theory-law progression, it must be highly confusing to learn that the crowning achievement of modern physics – itself the “queen of the sciences” – is a complex, extraordinarily precise, quantitative structure is known as the Standard Model of Particle Physics, not the Standard Theory, or the Standard Law!  The Standard Model incorporates three of the four major forces of nature, describes many subatomic particles, and has successfully predicted numerous subtle properties of subatomic particles.  Does this mean that “model” now implies a large, well-worked out and self-consistent body of scientific knowledge?  Not at all; in fact, “model” and “hypothesis” are used interchangeably at the simplest levels of experimental investigation in biology, neuroscience, etc., so definition-wise, we’re back to the beginning.

The reason that the Standard Model is a model and not a theory seems basically to be the same as the reason that the Alvarez Hypothesis is a hypothesis and not a theory or that Evolution is a theory and not a law:  essentially it is a matter of convention, tradition, or convenience.  The designations, we can infer, are primarily names that lack exact substantive, generally agreed-on definitions.

So, rather than worrying about any profound distinctions between hypotheses, theories, laws (and models) it might be more helpful to look at the properties that they have in common:

1. They are all “conjectural” which, for the moment, means that they are inventions of the human mind.

2. They make specific predictions that are empirically testable, in principle.

3. They are falsifiable – if their predictions are false, they are false – though not provable, by experiment or observation. 

4.  As a consequence of point 3., hypotheses, theories, and laws are all provisional; they may be replaced as further information becomes available. 

“Hypothesis,” it seems to me, is the fundamental unit, the building block, of scientific thinking. It is the term that is most consistently used by all sciences; it is more basic than any theory; it carries the least baggage, is the least susceptible to multiple interpretations and, accordingly, is the most likely to communicate effectively.  These advantages are relative of course; as I’ll get into elsewhere, even “hypothesis” is the subject of misinterpretation. In any case, its simplicity and clarity are why this website is devoted to the Scientific Hypothesis and not the others.

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Difference between Hypothesis and Theory

• Categorized under Science | Difference between Hypothesis and Theory

theory

The term hypothesis is used to refer to an explanation of things that occur. In some cases, it may refer to a simple guess. In other instances it may be a well-developed set of propositions that are crafted to explain the detailed workings of some occurrence or occurrences. One definition states specifically that it is the antecedent to a conditional proposition.

The hypothesis is formed and tested within the scientific process . One may develop the hypothesis while observation is occurring, but that may also be considered premature. The act of observation (outside of experimentation) may actually present opportunity to disprove a hypothesis. The hypothesis though is necessarily well defined and inclusive of details. This allows for accurate testing. It also in many cases distinguishes it from a theory.

The term theory is one of a rather scientific nature, but of a less limited nature. Some uses can refer to explanations of occurrences; some do include usage as referencing a simple guess. There is more though. Theory is used to refer to a branch of study that is focused on the general and conceptual, as compared to the practical and the applied of the same subject. It is significant that a theory is conjectural in nature.

Within the scientific process, the use of a theory is like a working model or understanding of what is occurring. The theory is often developed in the course of observation (in a non-experiment setting). Though, it is further developed by experimenting and the testing of hypotheses, a theory is only a theory. By its existence it maintains its validity. Once a theory is disproved, it is usually dismissed.

An illustration of sorts: If one watches water fall from a table after being spilled, one might develop the theory that water moves toward the floor. Then a hypothesis may be developed that states, water will move toward the flooring regardless of its direction relative to the table. Then testing of the hypothesis might include holding samples of the flooring in numerous directions relatively to the table and then releasing the same amount of water with the same vector on the table. If the water does not move upward from the edge of the table toward the flooring above the table, the hypothesis is incorrect and must be replaced.

Those are the major distinctions of theory and hypothesis and their similarities.

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Cite APA 7 lance, r. (2017, July 18). Difference between Hypothesis and Theory. Difference Between Similar Terms and Objects. http://www.differencebetween.net/science/difference-between-hypothesis-and-theory/. MLA 8 lance, raa. "Difference between Hypothesis and Theory." Difference Between Similar Terms and Objects, 18 July, 2017, http://www.differencebetween.net/science/difference-between-hypothesis-and-theory/.

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The effect of information provision on consumers’ risk perceptions of, support for a ban, and behavioral intention towards the preventive use of antibiotics in food animals

  • Yingnan Zhou 1 , 2 ,
  • Airong Zhang 1 ,
  • Rieks Dekker van Klinken 1 &
  • Junxiu Wang 3 , 4  

BMC Public Health volume  24 , Article number:  1428 ( 2024 ) Cite this article

Metrics details

Antibiotics have been widely used in feed and drinking water for food animals to prevent them from getting sick. Such preventive use of antibiotics has become a contributor to increasing antibiotic resistance and thus poses threats to human health. However, consumers have little knowledge about this practice and the associated health risks of increasing transmission of antibiotic residues and antibiotic resistant bacteria. This study aimed to examine the effect of information provision on consumers’ risk perceptions, support for a ban, and behavioral intention regarding the preventive use of antibiotics in food animals. Especially, the study sought to test two competing hypotheses which were informed by two theoretical perspectives of fear appeal theory — the linear model and the plateau effect model. The former suggested that providing information on the health risks of both antibiotic residues and antibiotic resistant bacteria would have a stronger effect compared to providing information on only one of them, while the latter posited that providing information on both risks might not have additional influence, as the effect of information on either risk could reach the plateau.

An experimental study with four conditions was conducted where participants read different information on the health risks associated with the preventive use first and then answered questions regarding consumers’ risk perceptions, support for a ban, and behavioral intention regarding the preventive use. Condition 1 was the control condition, where basic information about antibiotics, antibiotic resistance, and the preventive use was provided. Condition 2 and Condition 3 further added information on the health risk of antibiotic residues (Condition 2) and antibiotic resistant bacteria (Condition 3) due to the preventive use, respectively. Condition 4 provided all information contained in the first three conditions.

The results showed that compared to participants in the control condition, participants in Conditions 2-4 reported higher risk perceptions, stronger support for a ban on the preventive use, and a higher intention to buy meat produced without the preventive use of antibiotics. However, there were no significant differences in these factors between Conditions 2-4, indicating that providing information on the health risk of either antibiotic residues, or antibiotic resistant bacteria, or both, has similar effect on these variables. That is, the hypothesis based on the plateau effect model was supported.

Conclusions

The findings suggested that informing the public with the health risk of either antibiotic residues or antibiotic resistant bacteria associated with the preventive use is effective enough to reach plateau effect in increasing risk perceptions, support for a ban, and behavioral intention, which has important implications for policymakers and livestock industries to develop effective communication strategies to promote responsible antibiotic use in food animals.

Peer Review reports

Antibiotic resistance has posed a serious health threat worldwide. New antibiotic resistant bacteria are emerging and spreading globally, leading to increased mortality and higher health care burden [ 1 ]. It was estimated that antibiotic resistant infections were responsible for 1.27 million deaths in 2019 [ 2 ]. Without urgent action, the figure is projected to reach 10 million deaths every year by 2050 [ 1 ]. Moreover, antibiotic resistant bacteria are spreading within and between different ecosystems, thus has become a global ecological problem affecting the health of humans, animals, and the environment [ 3 , 4 ]. Antibiotic resistance is largely accelerated by the inappropriate use and overuse of antibiotics in multiple sectors, especially in humans and food animals [ 5 ]. The use of antibiotics and the action taken to control antibiotic resistance in one sector affects the others [ 5 , 6 ]. Therefore, to address the health threat posed by increasing antibiotic resistance, it’s necessary to take a One Health approach [ 3 , 7 , 8 ]. One Health is an integrated and unifying approach that mobilizes collaborative effort of multiple sectors, disciplines, and communities to sustainably balance and optimize the health of humans, animals, and the environment [ 9 ]. From One Health perspective, it’s important to reduce the inappropriate use of antibiotics in both humans and food animals. Although a growing body of evidence has demonstrated the link between antibiotic use in food animals and increasing antibiotic resistance [ 10 , 11 , 12 , 13 ], compared to antibiotic misuse and antibiotic resistance in humans, inappropriate use of antibiotics and antibiotic resistance in food animals has drawn less attention [ 14 ].

Antibiotics administered to food animals include therapeutic use and subtherapeutic use. Therapeutic use refers to using antibiotics to treat infectious diseases in sick animals [ 15 ]. The therapeutic use of antibiotics is essential for treating bacterial infections and protecting animal welfare. Subtherapeutic use refers to administering antibiotics to animals with doses lower than therapeutic use for a longer period to prevent healthy animals from getting sick or to promote growth [ 16 , 17 , 18 ]. In this case, antibiotics are usually added to feed or water [ 19 , 20 ]. Recently, many countries (e.g., the EU, the US, and China) have banned antibiotics from being used as growth promoters [ 21 , 22 , 23 ].

The preventive use of antibiotics was, however, only banned in the EU countries since 2022 and is still allowed in most countries globally [ 19 , 24 ]. Farmers routinely added antibiotics in feed or water to prevent diseases in groups of animals in various countries such as Brazil [ 25 ], Cambodia [ 26 ], Thailand [ 27 ], Vietnam [ 28 ], and China [ 29 ]. In Thailand, an average of 303 mg of antibiotics including tilmicosin, doxycycline, amoxicillin, colistin, and oxytetracycline were given to each chicken for disease prevention during the 41 days of raising period [ 27 ]. In Brazil, antibiotics were widely used for disease prevention among sows, newborn piglets, and weaning pigs [ 25 ]. Farmers alternately used antibiotics of different classes (e.g., aminopenicillin, pleuromutilin, amphenicol, polymyxin, tetracycline, quinolone, and macrolide) to prevent diseases in piglets. As a result, a piglet could be exposed to more than five antibiotic classes between 28 and 70 days of life [ 25 ]. Such preventative use of antibiotics in healthy animals may have serious consequences for human health [ 5 , 30 ]. Therefore, the present study focused on the preventive use of antibiotics in food animals.

The health risk associated with antibiotic residues due to the preventive use of antibiotics in food animals

The preventive use of antibiotics in food animals may result in antibiotic residues presenting in food [ 16 , 31 , 32 ]. Antibiotic residues have been found in various animal-derived food, such as meat, fish, milk, and eggs in many countries and regions globally (e.g., the US, Brazil, Cameroon, Egypt, Ghana, Greece, Nigeria, Bangladesh, Zambia, Iran, Kenya, China, India, and South Africa) [ 32 , 33 ]. High concentration of antibiotic residues in food can have direct toxic effects on human beings [ 16 , 31 , 32 ]. Among those, allergic reactions against β -lactam antibiotic (e.g., cephalosporin and penicillin) residues in meat or milk are most common [ 6 ]. The symptoms may include acute interstitial nephritis, vasculitis, skin rashes, bronchospasm, acute interstitial nephritis, vasculitis, serum sickness, erythema multiforme, toxic epidermal necrolysis, hemolytic anemia, anaphylaxis thrombocytopenia, angioedema, and Stevens–Johnson syndrome [ 6 , 34 ]. Antibiotic residues in food may also damage the immune system, organs (i.e., liver, kidneys, and reproductive organs), and bone marrow, as well as increase the chance of mutations and carcinoma [ 31 , 35 , 36 ].

The health risk associated with antibiotic resistant bacteria due to the preventive use of antibiotics in food animals

The preventive use of antibiotics in food animals has become a great contributor to the increase of antibiotic resistant bacteria both in animals and in the environment, thus increases the risk of humans getting infected with antibiotic resistant bacteria [ 37 , 38 , 39 , 40 ]. Studies have found a strong association between the prevalence of antibiotic resistant bacteria in food animals and in human beings [ 41 , 42 ]. The most common antibiotic resistant foodborne bacteria affecting human health are antibiotic resistant E. coli, salmonella, campylobacters, and enterococci [ 16 , 43 ]. A growing body of evidence has shown an increasing prevalence of these antibiotic resistant bacteria in animals. For instance, Roth et al. investigated the prevalence of antibiotic resistant bacteria in poultry in the US, China, Brazil, and the EU. They found the average proportion of antibiotic resistant E. coli isolated from chickens was over 40% [ 44 ]. Van Boeckel et al. revealed that the prevalence of antibiotic resistant bacteria (i.e., E. coli, campylobacters, salmonella, and staphylococcus aureus) in chickens, pigs, and cattle has all largely increased from 2000 to 2018 in low- and middle-income countries [ 14 ]. Furthermore, animals cannot fully metabolize the administered antibiotics. Consequently, antibiotic residues and antibiotic resistant bacteria are discharged into the environment (i.e., water and soils) through manure [ 20 , 45 , 46 ]. High level of antibiotic resistant bacteria has been found in almost all parts of the environment, including soil [ 47 , 48 ], freshwater aquaculture ponds [ 49 ], rivers [ 50 ], groundwater [ 51 ], sediments and sea water [ 52 ]. Consequently, humans may get infected with antibiotic resistant bacteria when they have direct contact with animals, handle contaminated food, consume undercooked food, consume contaminated water and vegetables, or have contact with antibiotic resistant bacteria in the environment [ 4 , 53 ]. If humans are infected with antibiotic resistant bacteria, it would be more difficult, or even impossible to treat as existing antibiotics have become ineffective in treating these bacterial infections [ 1 ]. For instance, antibiotic resistant foodborne bacteria E. coli and Staphylococcus aureus infections were responsible for over 500, 000 deaths in 2019 [ 2 ].

Consumers’ knowledge about, perceptions of, and attitudes towards the preventive use of antibiotics in food animals

Research so far has mainly focused on investigating consumers’ knowledge about, perceptions of, and attitudes towards the overall use of antibiotics in food animals [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ]. The results of these studies suggested that, although having little knowledge about antibiotic use in food animals and its contribution to antibiotic resistance, consumers somehow believed antibiotics are widely used in livestock industries and concerned about it [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 62 , 63 ]. Moreover, the public concern about overall antibiotic use in food animals has facilitated the purchase demand and higher willingness to pay for animal-derived food produced without any use of antibiotics [ 66 , 67 ]. In response to this consumer demand, “antibiotic-free” or “raised without antibiotics” labelled food products have emerged in many countries (e.g., the US, Germany, the UK, Italy, and Australia) [ 55 , 56 , 68 , 69 ]. However, removing therapeutic use of antibiotics in livestock is detrimental to both animal welfare and food safety [ 55 , 70 ]. Therefore, from the perspective of One Health, it’s of extreme importance to promote responsible antibiotic use in food animals rather than eliminating antibiotics altogether, as it can balance and optimize the health of humans, animals, and the environment. The purchase demand for antibiotic-free animal-derived food might be due to concern about food safety and preference for less chemicals and additives in food [ 64 , 71 , 72 ], the lack of knowledge about the difference between therapeutic and subtherapeutic use of antibiotics, and the misunderstanding that all antibiotics used in food animals are harmful [ 73 ].

Hence, it’s necessary to differentiate the preventative use from therapeutic use of antibiotics, and to investigate consumers’ knowledge about, perceptions of, and attitudes towards the preventive use of antibiotics separately. However, only limited studies have shed some light on this area. Research in Ireland found most consumers were unfamiliar with the preventive use of antibiotics in food animals and were surprised when being informed about it, because they thought that antibiotics can only be used for treatments [ 62 ]. On the other hand, some consumers considered the preventive use of antibiotics as a normative and standard practice in farming [ 62 ]. Research in the US revealed that only about one third of consumers were very concerned about the preventive use of antibiotics in food animals and even less of them considered the preventive use as unacceptable [ 61 ]. These findings suggested that consumers have little knowledge about the preventive use of antibiotics in food animals and limited understanding of the health risks associated with it.

Emerging experimental studies suggested that receiving information on the health risk of antibiotic resistance associated with antibiotic use in food animals could significantly increase consumers’ knowledge and risk perception regarding antibiotic use and antibiotic resistance in food animals [ 60 , 74 ] as well as willingness to pay for antibiotic-free animal-derived food [ 66 , 67 ]. However, some of these studies focused on the hazard of using antibiotics for promoting growth [ 66 ]. While others focused on the health risk in relation to the overall use of antibiotics in food animals without differentiating the preventative use and therapeutic use [ 60 , 67 , 74 ].

The present study

Given that consumers have little knowledge about the preventive use of antibiotics in food animals (hereafter referred to as “the preventive use”), this study sought to improve consumers’ knowledge through providing information on the health risks associated with the preventive use, and investigating its effect on risk perceptions, support for a ban, and behavioral intention. The current research applied quasi-experimental methodology [ 75 , 76 ] to systematically present information on the health risks associated with the preventive use. This allowed us to explore if variations in information can affect consumers’ risk perceptions, support for a ban, and behavioral intention regarding the preventive use.

We anticipated that increased knowledge about the health risks of antibiotic residues and antibiotic resistant bacteria associated with the preventive use would influence risk perceptions, support for a ban, and behavioral intention towards the preventive use. We could not assume whether there would be significant differences in the effects of increased knowledge on antibiotic residues only versus knowledge on antibiotic resistant bacteria only, as there is no existing literature allowing us to make any assumptions.

Further, we hypothesized that, compared to providing knowledge on either antibiotic residues only or antibiotic resistant bacteria only, providing knowledge on both antibiotic residues and antibiotic resistant bacteria associated with the preventive use would either lead to a stronger effect on changes in risk perceptions, support for a ban, and behavioral intention, or result in no further increase. These competing hypotheses were informed by research based on fear appeal theory. Fear appeal is a persuasive communication strategy aiming at promoting attitude and behavioral changes by arousing fear via emphasizing the potential risk [ 77 , 78 ]. Research has suggested two theoretical perspectives of fear appear — the linear model and the plateau effect model. The linear model posits a positive linear relationship between depicted fear and persuasion, such that the more fear depicts, the more effective the information is in affecting risk perceptions, attitudes, and behavioral intentions [ 79 , 80 , 81 , 82 , 83 ]. From the perspective of the linear model, providing information about the health risks of both antibiotic residues and antibiotic resistant bacteria would have a stronger effect compared to providing information on only one of them. On the other hand, the plateau effect model suggests that the effect of depicted fear will reach a plateau at certain point, beyond which depicting additional fear has no additional influence on risk perceptions, attitudes, intentions, and behaviors [ 78 , 84 ]. From this perspective, providing information on both antibiotic residues and antibiotic resistant bacteria may not have additional influence, as it is likely that the effect of increased knowledge on either antibiotic residues only or antibiotic resistant bacteria only would reach its plateau.

Taken together, we hypothesized that providing information on the health risk of either antibiotic residues, or antibiotic resistant bacteria, or both caused by the preventive use would significantly increase consumers’ risk perceptions, support for a ban, and behavioral intention regarding the preventive use. We further hypothesized that providing information on the health risks of both antibiotic residues and antibiotic resistant bacteria would have an either stronger or similar effect on these variables in comparison with providing information on only one of them.

Research design

An experimental study with four conditions was employed via an online survey in China. Condition 1 was the control condition, where participants read background information (i.e., definitions of antibiotics, antibiotic resistance, and the preventive use of antibiotics in food animals). Condition 2 further provided information on the effect of the preventive use on human health via increasing antibiotic residues in food, in addition to the information provided in Condition 1. Condition 3 provided information on the effect of the preventive use on human health via increasing the risk of getting infected with antibiotic resistant bacteria, in addition to the information provided in Condition 1. Condition 4 provided all information contained in the first three conditions. The provided information was developed based on findings in previous studies [ 1 , 4 , 6 , 10 , 11 , 15 , 19 , 32 , 44 , 53 , 85 ]. Figure  1 outlines the information provided for each condition and the underlying rationale. The experimental material is presented in Table  1 .

figure 1

Experimental design diagram and rationale

Participants were randomly assigned to one of the four conditions and were then asked to rate on a number of questions regarding risk perceptions (i.e., concern about antibiotic residues and antibiotic resistant bacteria, fear towards use of antibiotics as a preventative in food animals), support for a ban, and behavioral intention towards the preventive use (i.e., intention to buy meat produced without the preventive use of antibiotics).

Procedure and participants

A professional online research platform (Credamo) was used to collect data. The survey link was sent to the participants panel of the research platform. Participants read the information and consent sheet first, which included a brief introduction to the study, information regarding participation and withdrawal (i.e., the participation is voluntary and participants can withdraw at any time), risks and benefits (i.e., no foreseeable risks), confidentiality (i.e., no personally identifiable information will be collected and all collected information will be treated confidentially), and contacts. Participants were asked to click ‘Next page’ button if they consent to take part in the survey. After answering the questions on demographics (i.e., gender, age group, and education), participants were randomly assigned to one of the four conditions, and then answered the questions on the dependent variables (i.e., risk perceptions, support for a ban, and behavioral intention regarding the preventive use). To ensure the participants would read the provided information carefully, timers were included. For participants in Conditions 1-4, the information was displayed on the page for 45, 60, 90, and 100 s respectively before being able to move on. The timers increased as the length of information increased, which was informed by the pretesting within the research team. A small fee was paid to participants who completed the survey.

A total of 2533 participants across China completed the survey. The majority of them were female (61.4%), at the age of 18 to 44 years (80.6%), and had completed at least a bachelor’s degree (80.6%). Table  2 presents participants’ demographic information for each condition.

A 5-point Likert scale (1 = Strongly disagree, 5 = Strongly agree; unless stated otherwise) was provided for all responses. Cronbach’s alpha was calculated for multi-item measurements to examine the reliability of these measurements. An α value of 0.70 and above was considered acceptable [ 86 , 87 ]. The average scores of items for multi-item measurements were used in data analysis.

Three aspects were measured to examine participants’ risk perceptions: concern about antibiotic residues, concern about antibiotic resistant bacteria, and fear towards use of antibiotics as a preventative in food animals. Concern about antibiotic residues was measured by asking participants to indicate their degree of agreement with four statements adapted from Michaelidou and Hassan [ 88 ] : “I think most meat contain antibiotic residues,” “I’m concerned about the amount of antibiotic residues in meat,” “Antibiotic residues are widespread in the environment,” “I’m concerned about the amount of antibiotic residues in the environment,” ( α = 0.76). Concern about antibiotic resistant bacteria was measured by asking participants to indicate their degree of agreement with four statements adapted from Michaelidou and Hassan [ 88 ] : “I think most meat contain antibiotic resistant bacteria,” “I’m concerned about the amount of antibiotic resistant bacteria in meat,” “Antibiotic resistant bacteria are widespread in the environment,” “I’m concerned about the amount of antibiotic resistant bacteria in the environment,” ( α = 0.77). Fear towards use of antibiotics as a preventative in food animals was measured by asking participants to express their feelings of fear (frightened, anxious, and worried) towards use of antibiotics as a preventative in food animals (1 = Not at all, 5 = Very much; α = 0.83) (Adapted from Milne et al. [ 89 ]).

Supporting a ban for the preventive use was measured with “Please indicate to what extent do you support a ban for the preventive use of antibiotics in food animals?” (1 = I don’t support at all, 5 = I totally support) (Adapted from Lusk et al. [ 90 ]).

Intention to buy meat produced without the preventive use of antibiotics was measured by asking participants to indicate their degree of agreement with two statements adapted from Bradford et al. [ 54 ]: “I intend to buy meat produced without the preventive use of antibiotics” and “I will look for meat produced without the preventive use of antibiotics” ( α = 0.75).

Data analysis

Data analysis was conducted by using SPSS version 22.0. One-way analysis of variance (ANOVA) was utilized to test the differences in demographics and the dependent variables between the four conditions. For variables where significant differences were found between the four conditions, Tukey (when equal variance assumption was satisfied) and Games-Howell (when equal variance assumption was not satisfied) post-hoc comparisons with bias-corrected and accelerated bootstrap estimation (1,000 samples) were carried out. A 95% confidence interval (CI) of the difference between means was used to determine whether the difference was significant. A 95% CI without zero indicates that the difference is statistically significant.

A series of one-way between-subjects ANOVA analyses were first conducted to examine the differences in demographics (i.e., gender, age, and education). The results suggested that there were no significant differences in these demographic variables among the four conditions: gender, F (3, 2529) = 0.63, p = 0.598; age, F (3, 2529) = 0.93, p = 0.428; education, F (3, 2529) = 0.95, p = 0.417. These results indicated that any differences in the dependent variables between the four conditions were very likely due to the differences in information provision.

Another series of one-way ANOVA were carried out to further examine the differences in dependent variables (i.e., risk perceptions, support for a ban, and behavioral intention regarding the preventive use) across the four conditions. The results revealed significant differences among the four conditions in concern about antibiotic residues, F (3, 2529) = 11.11, p < 0.001, η p 2 = 0.013, concern about antibiotic resistant bacteria, F (3, 2529) = 7.38, p < 0.001, η p 2 = 0.009, fear towards use of antibiotics as a preventative in food animals, F (3, 2529) = 21.48, p < 0.001, η p 2 = 0.025, supporting a ban for the preventive use, F (3, 2529) = 12.47, p < 0.001, η p 2 = 0.015, and intention to buy meat produced without the preventive use of antibiotics, F (3, 2529) = 7.10, p < 0.001, η p 2 = 0.008. Post-hoc comparisons indicated that all these variables were significantly lower in Condition 1 than in all other conditions (Fig.  2 ), all ps < 0.05, all 95% CIs of the differences between means did not include 0. However, there were no significant differences in these variables between Conditions 2-4 (all ps > 0.05, all 95% CIs of the differences between means included 0). The descriptive statistics of dependent variables, the correlations between dependent variables and with demographics, and the 95% CIs of the differences between means of dependent variables in the four conditions are presented in Appendices A , B , and C , respectively.

figure 2

Means of the dependant variables with error bar. Note * p < 0.05, ** p < 0.01, *** p < 0.001. Concern about antibiotic residues, concern about antibiotic resistant bacteria, and intention to buy meat produced without the preventive use of antibiotics were measured on a 5-point scale (1 = Strongly disagree, 5 = Strongly agree). Fear towards use of antibiotics as a preventative in food animals was measured on a 5-point scale (1 = Not at all, 5 = Very much). Supporting a ban for the preventive use of antibiotics in food animals was measured on a 5-point scale (1 = I don’t support at all, 5 = I totally support)

The preventive use of antibiotics in food animals poses serious threats to human health globally. Previous research, however, suggested consumers have little knowledge about this practice. This study sought to examine the effect of information provision on the risk perceptions of, support for a ban, and behavioral intention towards the preventive use through an experimental study.

The results suggested that providing information on the health risks caused by the preventive use has significant influence on consumers’ risk perceptions, support for a ban, and behavioral intention regarding the preventive use. Compared to participants in control condition, where no health risk information was provided (Condition 1), participants who received information on the health risk of antibiotic residues (Condition 2), antibiotic resistant bacteria (Condition 3), and both antibiotic residues and antibiotic resistant bacteria (Condition 4) associated with the preventive use reported significantly higher level of risk perceptions of the preventive use (i.e., concern about antibiotic residues and antibiotic resistant bacteria, fear towards use of antibiotics as a preventative in food animals), stronger support for a ban on the preventive use, and a higher intention to buy meat produced without the preventive use. These findings demonstrated that increasing knowledge about the health risks of the preventive use was influential in increasing risk perceptions, support for a ban, and behavioral intention regarding the preventive use.

While there is no pre-existing literature allowing us to make assumptions about what differences it would make by providing participants with information on antibiotic residues or antibiotic resistant bacteria, this study revealed that information on the health risk of either antibiotic residues or antibiotic resistant bacteria led to similar levels of changes in risk perceptions, support for a ban, and behavioral intention in comparison to no health risk information being provided. That is, providing information on the health risk associated with either antibiotic residues or antibiotic resistant bacteria is equally effective in affecting these variables. Interestingly, when only information on the risk of antibiotic residues was provided, consumers’ concern about antibiotic resistant bacteria was also significantly increased, and vice versa. A possible explanation is that information on either of them could increase consumers’ overall risk perceptions of the preventive use. Therefore, providing information on the health risk of either antibiotic residues or antibiotic resistant bacteria enhanced the risk perceptions for both of them, despite that antibiotic residues and antibiotic resistant bacteria represented two different pathways in affecting human health. Besides, due to the abstract nature of antibiotic resistance, the understanding of this health threat was limited, resulting in misconceptions and low risk perceptions among the public [ 91 , 92 , 93 ]. Hence, it is likely that providing information about the transmission risks of either antibiotic residues or antibiotic resistant bacteria from animals to humans helped to make the issue of antibiotic resistance less abstract to the participants, thus increased the risk perception for both.

Furthermore, our findings suggested that providing information on both health risks in antibiotic residues and antibiotic resistant bacteria has a similar effect on consumers’ risk perceptions, support for a ban, and behavioral intention as providing information on only one of them. Although the pathways of how antibiotic residues and antibiotic resistant bacteria affect human health differ, the results demonstrated that providing information on both did not have an additive effect. This finding supported the hypothesis based on the plateau effect model rather than the linear model [ 78 , 84 ]. That is, providing information on the health risk of either antibiotic residues or antibiotic resistant bacteria is effective enough to reach the plateau effect. Consequently, there is no significant additional effect by depicting both risks. Thus, consumers’ exposure to either information led to the greatest changes in risk perceptions, support for a ban, and behavioral intention.

The findings of the current study have important implications for livestock industries and policymakers. First, the findings provide insights for developing effective risk communication strategies to increase public risk perceptions and promote attitude and behavioral intention changes regarding the preventive use. Future risk communication can convey simple messages about the health risk of either antibiotic residues or antibiotic resistant bacteria associated with the preventive use, as exposure to information on either of them is influential. Noticeably, though the results of the present study indicated a “plateau effect” of information provision, it needs to be cautious when applying the findings to campaigns in the real world. Future research needs to further validate the results and explore if the “plateau effect” holds true in other scenarios. For instance, the information we provided on the health risks of both antibiotic residues and antibiotic resistant bacteria might be too long for the participants to fully process in the survey setting. Future research can provide information on both via shorter message and examine if there is additional effect. In addition, the effect of using video instead of text to convey the information should be examined, as video might be more influential than text [ 73 ]. Moreover, to make the health risks of antibiotic residues and antibiotic resistant bacteria more realistic and relevant to the public, future research can include storytelling from people who have been affected by antibiotic residues or antibiotic resistant infections [ 94 ]. Further, future studies can also explore the moderating variables between the relationship of information provision and consumers’ risk perception, support for a ban, and behavioral intention. For example, animal-derived food products produced without the preventive use of antibiotics might be more expensive than conventional products. Therefore, participants’ income level might be a moderator. Health literacy is also a potential moderator. It is the capacity to access, understand, evaluate, and use information to maintain or improve health and quality of life [ 95 ]. Low health literacy is related to less healthy choices and riskier behaviors [ 95 ]. It’s possible that information provision is more effective among people who have higher level of health literacy as they might be able to understand the provided information better and make a healthier purchase decision. Second, this study has important implications for antibiotic stewardship in food animals. The findings indicated that once the public is aware of the health risks posed by the preventive use, they express a stronger demand to ban the practice, which challenges the industries’ social license to operate. Such public demand will help facilitate the implementation of a ban on this practice. Further, this research highlights a potential market for animal-derived food produced without the preventive use of antibiotics among informed consumers, which could incentivize livestock industries to use antibiotics responsibly. This consumer preference is of great value for promoting responsible antibiotic use in food animals from One Health perspective, especially considering that the increasing concern about antibiotic overuse in food animals might lead to consumer demand for eliminating antibiotics in livestock industries [ 55 , 56 , 69 ]. Given the severe health risks posed by the inappropriate use and overuse of antibiotics in food animals, the public needs to be made more aware of the issue. However, people shouldn’t be alarmist and overzealously seek to boycott necessary use of antibiotics, as it is harmful to both human health and animal welfare.

While the current research shed lights on how building consumer knowledge can enhance consumers’ risk perceptions, support for a ban, and behavioral intention regarding the preventive use, there are some limitations. For instance, there might be some vegetarians among the participants. We did not filter out and exclude them because the proportion of vegetarians in Chinese population is relatively small (about 4-5%) [ 96 ] and the number of them should be reasonably balanced across the four conditions via random assignment. Future research should consider excluding vegetarians from analysis.

To our best knowledge, this was the first study exploring the effect of information provision on risk perceptions, support for a ban, and behavioral intention regarding the preventive use of antibiotics in food animals. The findings demonstrated that providing information on the health risk of antibiotic residues, or antibiotic resistant bacteria, or both in relation to the preventive use is similarly effective in increasing consumers’ risk perceptions, support for a ban, and behavioral intention regarding this practice. These results suggested that increasing consumers’ knowledge about the health risk of either antibiotic residues or antibiotic resistant bacteria can lead to the greatest changes in these variables. The findings of the research can provide important insights to inform policymakers and livestock industries to develop effective communication strategies and public policies to promote responsible antibiotic use in food animals.

Descriptive statistics of dependent variables ( M±SD )

  • Note C1, Condition 1, C2, Condition 2, C3, Condition 3, C4, Condition 4. Concern about antibiotic residues, concern about antibiotic resistant bacteria, and intention to buy meat produced without the preventive use of antibiotics were measured on a 5-point scale (1 = Strongly disagree, 5 = Strongly agree). Fear towards use of antibiotics as a preventative in food animals was measured on a 5-point scale (1 = Not at all, 5 = Very much). Supporting a ban for the preventive use of antibiotics in food animals was measured on a 5-point scale (1 = I don’t support at all, 5 = I totally support)

Pearson correlations between dependent variables and with demographics

  • Note C1, Condition 1, C2, Condition 2, C3, Condition 3, C4, Condition 4. * p < 0.05, ** p < 0.01, *** p < 0.001. Gender, 1 = Male, 2 = Female. Age, 1 = 18-24 years, 2 = 25-34 years, 3 = 35-44 years, 4 = 45-54 years, 5 =55+ years. Education, 1 = Senior high school and below (year 12), 2 = College certificate, 3 = Bachelor’s degree, 4 = Postgraduate

Differences in means of dependent variables between the four conditions

  • Note C1, Condition 1, C2, Condition 2, C3, Condition 3, C4, Condition 4. M diff , Differences in mean. 95% CI, 95% confidence interval. * p < 0.05, ** p < 0.01, *** p < 0.001

Data availability

The datasets used in the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank Chris Angwin for proofreading this article.

This work was supported by Key Projects of Philosophy and Social Sciences Research, Ministry of Education of the People’s Republic of China (Award number: 21JZD038), the Trusted Agrifood Exports Mission program of Commonwealth Scientific and Industrial Research Organisation (CSIRO), and China Scholarship Council (CSC Award Number: 202004920045).

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Yingnan Zhou, Airong Zhang & Rieks Dekker van Klinken

School of Sociology and Ethnology, University of Chinese Academy of Social Sciences, Beijing, 102488, China

Yingnan Zhou

School of Mental Health, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China

Junxiu Wang

The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325007, China

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YZ, AZ, RDvK, and JW conceived and designed the study. YZ conducted data collection and data analysis. YZ and AZ wrote the first draft of the manuscript. RDvK and JW provided critical feedback for revisions. All authors reviewed and approved the final version of the manuscript.

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Correspondence to Junxiu Wang .

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Zhou, Y., Zhang, A., van Klinken, R.D. et al. The effect of information provision on consumers’ risk perceptions of, support for a ban, and behavioral intention towards the preventive use of antibiotics in food animals. BMC Public Health 24 , 1428 (2024). https://doi.org/10.1186/s12889-024-18859-2

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DOI : https://doi.org/10.1186/s12889-024-18859-2

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