unscientific hypothesis example

The Unfalsifiable Hypothesis Paradox

What is the unfalsifiable hypothesis paradox.

Imagine someone tells you a story about a dragon that breathes not fire, but invisible, heatless fire. You grab a thermometer to test the claim but no matter what, you can’t prove it’s not true because you can’t measure something that’s invisible and has no heat. This is what we call an ‘unfalsifiable hypothesis’—it’s a claim that’s made in such a way that it can’t be proven wrong, no matter what.

Now, the paradox is this: in science, being able to prove or disprove a claim makes it strong and believable. If nobody could ever prove a hypothesis wrong, you’d think it’s completely reliable, right? But actually, in science, that makes it weak! If we can’t test a claim, then it’s not really playing by the rules of science. So, the paradox is that not being able to prove something wrong can make a claim scientifically useless—even though it seems like it would be the ultimate truth.

Key Arguments

An unfalsifiable hypothesis is a claim that can’t be proven wrong, but just because we can’t disprove it, that doesn’t make it automatically true.
Science grows and improves through testing ideas; if we can’t test a claim, we can’t know if it’s really valid.
Being able to show that an idea could be wrong is a fundamental part of scientific thinking. Without this testability, a claim is more like a personal belief or a philosophical idea than a scientific one.
An unfalsifiable hypothesis might look like it’s scientific, but it’s misleading since it doesn’t stick to the strict rules of testing and evidence that science needs.
Using unfalsifiable claims can block our paths to understanding since they stop us from asking questions and looking for verifiable answers.
The dragon with invisible, heatless fire: This is an example of an unfalsifiable hypothesis because no test or observation could ever show that the dragon’s fire isn’t real, since it can’t be detected in any way.
Saying a celestial teapot orbits the Sun between Earth and Mars: This teapot is said to be small and far enough away that no telescope could spot it. Because it’s undetectable, we can’t disprove its existence.
A theory that angels are responsible for keeping us gravitationally bound to Earth: Since we can’t test for the presence or actions of angels, we can’t refute the claim, making it unfalsifiable.
The statement that the world’s sorrow is caused by invisible spirits: It sounds serious, but if we can’t measure or observe these spirits, we can’t possibly prove this idea right or wrong.

Answer or Resolution

Dealing with the Unfalsifiable Hypothesis Paradox means finding a balance. We can’t just ignore all ideas that can’t be tested because some might lead to real scientific breakthroughs one day. On the other side, we can’t treat untestable claims as true science. It’s about being open to possibilities but also clear about what counts as scientific evidence.

Some people might say we should only focus on what can be proven wrong. Others think even wild ideas have their place at the starting line of science—they inspire us and can evolve into something testable later on.

Major Criticism

Some people criticize the idea of rejecting all unfalsifiable ideas because that could block new ways of thinking. Sometimes a wild guess can turn into a real scientific discovery. Plus, falsifiability is just one part of what makes a theory scientific. We shouldn’t throw away potentially good ideas just because they don’t fit one rule, especially when they’re still in the early stages and shouldn’t be held too tightly to any rules at all.

Another point is that some important ideas have been unfalsifiable at first but later became testable. So, we have to recognize that science itself can change and grow.

Practical Applications

You might wonder, “Why does this matter to me?” Well, knowing about the Unfalsifiable Hypothesis Paradox actually affects a lot of real-world situations, like how we learn things in school, the kinds of products we buy, and even the rules and laws that are made.

Education: By learning what makes science solid, students can tell the difference between real science and just a bunch of fancy words that sound scientific but aren’t based on testable ideas.
Consumer Protection: Sometimes companies try to sell things by using science-sounding claims that can’t be proven wrong—and that’s where knowing about unfalsifiable hypotheses helps protect us from buying into false promises.
Legal and Policy Making: For people who make laws or guide big decisions, understanding this concept helps them judge if a study or report is really based on solid science.

Scientific Hypothesis Examples

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A hypothesis is an educated guess about what you think will happen in a scientific experiment, based on your observations. Before conducting the experiment, you propose a hypothesis so that you can determine if your prediction is supported.

There are several ways you can state a hypothesis, but the best hypotheses are ones you can test and easily refute. Why would you want to disprove or discard your own hypothesis? Well, it is the easiest way to demonstrate that two factors are related. Here are some good scientific hypothesis examples:

Hypothesis: All forks have three tines. This would be disproven if you find any fork with a different number of tines.
Hypothesis: There is no relationship between smoking and lung cancer. While it is difficult to establish cause and effect in health issues, you can apply statistics to data to discredit or support this hypothesis.
Hypothesis: Plants require liquid water to survive. This would be disproven if you find a plant that doesn't need it.
Hypothesis: Cats do not show a paw preference (equivalent to being right- or left-handed). You could gather data around the number of times cats bat at a toy with either paw and analyze the data to determine whether cats, on the whole, favor one paw over the other. Be careful here, because individual cats, like people, might (or might not) express a preference. A large sample size would be helpful.
Hypothesis: If plants are watered with a 10% detergent solution, their growth will be negatively affected. Some people prefer to state a hypothesis in an "If, then" format. An alternate hypothesis might be: Plant growth will be unaffected by water with a 10% detergent solution.
Scientific Hypothesis, Model, Theory, and Law
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What Is a Hypothesis? (Science)
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Null Hypothesis Definition and Examples
What Are Examples of a Hypothesis?
How To Design a Science Fair Experiment
Null Hypothesis Examples
What 'Fail to Reject' Means in a Hypothesis Test
Middle School Science Fair Project Ideas
Effect of Acids and Bases on the Browning of Apples
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From the Editors

Notes from The Conversation newsroom

How we edit science part 1: the scientific method

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We take science seriously at The Conversation and we work hard to report it accurately. This series of five posts is adapted from an internal presentation on how to understand and edit science by our Australian Science & Technology Editor, Tim Dean. We thought you might also find it useful.

Introduction

If I told you that science was a truth-seeking endeavour that uses a single robust method to prove scientific facts about the world, steadily and inexorably driving towards objective truth, would you believe me?

Many would. But you shouldn’t.

The public perception of science is often at odds with how science actually works. Science is often seen to be a separate domain of knowledge, framed to be superior to other forms of knowledge by virtue of its objectivity, which is sometimes referred to as it having a “ view from nowhere ”.

But science is actually far messier than this - and far more interesting. It is not without its limitations and flaws, but it’s still the most effective tool we have to understand the workings of the natural world around us.

In order to report or edit science effectively - or to consume it as a reader - it’s important to understand what science is, how the scientific method (or methods) work, and also some of the common pitfalls in practising science and interpreting its results.

This guide will give a short overview of what science is and how it works, with a more detailed treatment of both these topics in the final post in the series.

What is science?

Science is special, not because it claims to provide us with access to the truth, but because it admits it can’t provide truth .

Other means of producing knowledge, such as pure reason, intuition or revelation, might be appealing because they give the impression of certainty , but when this knowledge is applied to make predictions about the world around us, reality often finds them wanting.

Rather, science consists of a bunch of methods that enable us to accumulate evidence to test our ideas about how the world is, and why it works the way it does. Science works precisely because it enables us to make predictions that are borne out by experience.

Science is not a body of knowledge. Facts are facts, it’s just that some are known with a higher degree of certainty than others. What we often call “scientific facts” are just facts that are backed by the rigours of the scientific method, but they are not intrinsically different from other facts about the world.

What makes science so powerful is that it’s intensely self-critical. In order for a hypothesis to pass muster and enter a textbook, it must survive a battery of tests designed specifically to show that it could be wrong. If it passes, it has cleared a high bar.

The scientific method(s)

Despite what some philosophers have stated , there is a method for conducting science. In fact, there are many. And not all revolve around performing experiments.

One method involves simple observation, description and classification, such as in taxonomy. (Some physicists look down on this – and every other – kind of science, but they’re only greasing a slippery slope .)

However, when most of us think of The Scientific Method, we’re thinking of a particular kind of experimental method for testing hypotheses.

This begins with observing phenomena in the world around us, and then moves on to positing hypotheses for why those phenomena happen the way they do. A hypothesis is just an explanation, usually in the form of a causal mechanism: X causes Y. An example would be: gravitation causes the ball to fall back to the ground.

A scientific theory is just a collection of well-tested hypotheses that hang together to explain a great deal of stuff.

Crucially, a scientific hypothesis needs to be testable and falsifiable .

An untestable hypothesis would be something like “the ball falls to the ground because mischievous invisible unicorns want it to”. If these unicorns are not detectable by any scientific instrument, then the hypothesis that they’re responsible for gravity is not scientific.

An unfalsifiable hypothesis is one where no amount of testing can prove it wrong. An example might be the psychic who claims the experiment to test their powers of ESP failed because the scientific instruments were interfering with their abilities.

(Caveat: there are some hypotheses that are untestable because we choose not to test them. That doesn’t make them unscientific in principle, it’s just that they’ve been denied by an ethics committee or other regulation.)

Experimentation

There are often many hypotheses that could explain any particular phenomenon. Does the rock fall to the ground because an invisible force pulls on the rock? Or is it because the mass of the Earth warps spacetime , and the rock follows the lowest-energy path, thus colliding with the ground? Or is it that all substances have a natural tendency to fall towards the centre of the Universe , which happens to be at the centre of the Earth?

The trick is figuring out which hypothesis is the right one. That’s where experimentation comes in.

A scientist will take their hypothesis and use that to make a prediction, and they will construct an experiment to see if that prediction holds. But any observation that confirms one hypothesis will likely confirm several others as well. If I lift and drop a rock, it supports all three of the hypotheses on gravity above.

Furthermore, you can keep accumulating evidence to confirm a hypothesis, and it will never prove it to be absolutely true. This is because you can’t rule out the possibility of another similar hypothesis being correct, or of making some new observation that shows your hypothesis to be false. But if one day you drop a rock and it shoots off into space, that ought to cast doubt on all of the above hypotheses.

So while you can never prove a hypothesis true simply by making more confirmatory observations, you only one need one solid contrary observation to prove a hypothesis false. This notion is at the core of the hypothetico-deductive model of science.

This is why a great deal of science is focused on testing hypotheses, pushing them to their limits and attempting to break them through experimentation. If the hypothesis survives repeated testing, our confidence in it grows.

So even crazy-sounding theories like general relativity and quantum mechanics can become well accepted, because both enable very precise predictions, and these have been exhaustively tested and come through unscathed.

The next post will cover hypothesis testing in greater detail.

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What is a scientific hypothesis?

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

A girl looks at plants in a test tube for a science experiment. What's her scientific hypothesis?

Hypothesis basics

What makes a hypothesis testable.

Types of hypotheses
Hypothesis versus theory

Additional resources

Bibliography.

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

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

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

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

Here are some examples of hypothesis statements:

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

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

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

Types of scientific hypotheses

Elementary-age students study alternative energy using homemade windmills during public school science class.

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

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

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

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

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

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

Scientific theory vs. scientific hypothesis

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

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

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

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

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

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

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

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

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

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

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

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

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Frequently asked questions

What is a hypothesis.

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

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

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

If there is no sampling frame available (e.g., people with a rare disease)
If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

Reproducing research entails reanalyzing the existing data in the same manner.
Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data .
A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity , because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

Convergent validity : The extent to which your measure corresponds to measures of related constructs
Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

Response variables (they respond to a change in another variable)
Outcome variables (they represent the outcome you want to measure)
Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

Explanatory variables (they explain an event or outcome)
Predictor variables (they can be used to predict the value of a dependent variable)
Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

Open-ended and flexible
Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
Unambiguous, getting straight to the point while still stimulating discussion
Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

Structured interviews : The questions are predetermined in both topic and order.
Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
Unstructured interviews : None of the questions are predetermined.
Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
Statistical generalization: You use specific numbers about samples to make statements about populations.
Causal reasoning: You make cause-and-effect links between different things.
Sign reasoning: You make a conclusion about a correlational relationship between different things.
Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Triangulation can help:

Reduce research bias that comes from using a single method, theory, or investigator
Enhance validity by approaching the same topic with different tools
Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

Data triangulation : Using data from different times, spaces, and people
Investigator triangulation : Involving multiple researchers in collecting or analyzing data
Theory triangulation : Using varying theoretical perspectives in your research
Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps:

First, the author submits the manuscript to the editor.
Reject the manuscript and send it back to author, or
Send it onward to the selected peer reviewer(s)
Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions.
Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

Both variables are on an interval or ratio level of measurement
Data from both variables follow normal distributions
Your data have no outliers
Your data is from a random or representative sample
You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

Your research questions and/or hypotheses
Your overall approach (e.g., qualitative or quantitative )
The type of design you’re using (e.g., a survey , experiment , or case study )
Your sampling methods or criteria for selecting subjects
Your data collection methods (e.g., questionnaires , observations)
Your data collection procedures (e.g., operationalization , timing and data management)
Your data analysis methods (e.g., statistical tests or thematic analysis )

A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

A positive correlation means that both variables change in the same direction.
A negative correlation means that the variables change in opposite directions.
A zero correlation means there’s no relationship between the variables.

Random error is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

If you have quantitative variables , use a scatterplot or a line graph.
If your response variable is categorical, use a scatterplot or a line graph.
If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

An explanatory variable is the expected cause, and it explains the results.
A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

A control group that receives a standard treatment, a fake treatment, or no treatment.
Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
Experimenter effects : unintentional actions by researchers that influence study outcomes.
Situational variables : environmental variables that alter participants’ behaviors.
Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

Only requires small samples
Statistically powerful
Removes the effects of individual differences on the outcomes

Disadvantages:

Internal validity threats reduce the likelihood of establishing a direct relationship between variables
Time-related effects, such as growth, can influence the outcomes
Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

Prevents carryover effects of learning and fatigue.
Shorter study duration.
Needs larger samples for high power.
Uses more resources to recruit participants, administer sessions, cover costs, etc.
Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

It’s caused by the independent variable .
It influences the dependent variable
When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

In single-stage sampling , you collect data from every unit within the selected clusters.
In double-stage sampling , you select a random sample of units from within the clusters.
In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

In a single-blind study , only the participants are blinded.
In a double-blind study , both participants and experimenters are blinded.
In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

Prepare and organize your data.
Review and explore your data.
Develop a data coding system.
Assign codes to the data.
Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

Grounded theory involves collecting data in order to develop new theories.
Ethnography involves immersing yourself in a group or organization to understand its culture.
Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
Phenomenological research involves investigating phenomena through people’s lived experiences.
Action research links theory and practice in several cycles to drive innovative changes.

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

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

The type of soda – diet or regular – is the independent variable .
The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

Discrete variables represent counts (e.g. the number of objects in a collection).
Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

The independent variable is the amount of nutrients added to the crop field.
The dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

A testable hypothesis
At least one independent variable that can be precisely manipulated
At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

How you will manipulate the variable(s)
How you will control for any potential confounding variables
How many subjects or samples will be included in the study
How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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July 1, 2021

If You Say ‘Science Is Right,’ You’re Wrong

It can’t supply absolute truths about the world, but it brings us steadily closer

By Naomi Oreskes

Group of scientists examining unseen object on a table.

The COVID crisis has led many scientists to take up arms (or at least keyboards) to defend their enterprise—and to be sure, science needs defenders these days. But in their zeal to fight back against vaccine rejection and other forms of science denial, some scientists say things that just aren't true—and you can't build trust if the things you are saying are not trustworthy.

One popular move is to insist that science is right —full stop—and that once we discover the truth about the world, we are done. Anyone who denies such truths (they suggest) is stupid, ignorant or fatuous. Or, as Nobel Prize–winning physicist Steven Weinberg said, “Even though a scientific theory is in a sense a social consensus, it is unlike any other sort of consensus in that it is culture-free and permanent.” Well, no. Even a modest familiarity with the history of science offers many examples of matters that scientists thought they had resolved, only to discover that they needed to be reconsidered. Some familiar examples are Earth as the center of the universe, the absolute nature of time and space, the stability of continents, and the cause of infectious disease.

Science is a process of learning and discovery, and sometimes we learn that what we thought was right is wrong. Science can also be understood as an institution (or better, a set of institutions) that facilitates this work. To say that science is “true” or “permanent” is like saying that “marriage is permanent.” At best, it's a bit off-key. Marriage today is very different from what it was in the 16th or 18th century, and so are most of our “laws” of nature.

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Some conclusions are so well established we may feel confident we won't be revisiting them. I can't think of anyone I know who thinks we will be questioning the laws of thermodynamics any time soon. But physicists at the start of the 20th century, just before the discovery of quantum mechanics and relativity, didn't think they were about to rethink their field's foundations, either.

Another popular move is to say scientific findings are true because scientists use “the scientific method.” But we can never actually agree on what that method is. Some will say it is empiricism: observation and description of the world. Others will say it is the experimental method: the use of experience and experiment to test hypotheses. (This is cast sometimes as the hypothetico-deductive method, in which the experiment must be framed as a deduction from theory, and sometimes as falsification, where the point of observation and experiment is to refute theories, not to confirm them.) Recently a prominent scientist claimed the scientific method was to avoid fooling oneself into thinking something is true that is not, and vice versa.

Each of these views has its merits, but if the claim is that any one of these is the scientific method, then they all fail. History and philosophy have shown that the idea of a singular scientific method is, well, unscientific. In point of fact, the methods of science have varied between disciplines and across time. Many scientific practices, particularly statistical tests of significance, have been developed with the idea of avoiding wishful thinking and self-deception, but that hardly constitutes “the scientific method.” Scientists have bitterly argued about which methods are the best, and, as we all know, bitter arguments rarely get resolved.

In my view, the biggest mistake scientists make is to claim that this is all somehow simple and therefore to imply that anyone who doesn't get it is a dunce. Science is not simple, and neither is the natural world; therein lies the challenge of science communication. What we do is both hard and, often, hard to explain. Our efforts to understand and characterize the natural world are just that: efforts. Because we're human, we often fall flat. The good news is that when that happens, we pick ourselves up, brush ourselves off, and get back to work. That's no different from professional skiers who wipe out in major races or inventors whose early aspirations go bust. Understanding the beautiful, complex world we live in, and using that knowledge to do useful things, is both its own reward and why taxpayers should be happy to fund research.

Scientific theories are not perfect replicas of reality, but we have good reason to believe that they capture significant elements of it. And experience reminds us that when we ignore reality, it sooner or later comes back to bite us.

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Falsifiability

Karl popper's basic scientific principle, karl popper's basic scientific principle.

Falsifiability, according to the philosopher Karl Popper, defines the inherent testability of any scientific hypothesis.

This article is a part of the guide:

Inductive Reasoning
Deductive Reasoning
Hypothetico-Deductive Method
Scientific Reasoning
Testability

Browse Full Outline

1 Scientific Reasoning
2.1 Falsifiability
2.2 Verification Error
2.3 Testability
2.4 Post Hoc Reasoning
3 Deductive Reasoning
4.1 Raven Paradox
5 Causal Reasoning
6 Abductive Reasoning
7 Defeasible Reasoning

Science and philosophy have always worked together to try to uncover truths about the universe we live in. Indeed, ancient philosophy can be understood as the originator of many of the separate fields of study we have today, including psychology, medicine, law, astronomy, art and even theology.

Scientists design experiments and try to obtain results verifying or disproving a hypothesis, but philosophers are interested in understanding what factors determine the validity of scientific endeavors in the first place.

Whilst most scientists work within established paradigms, philosophers question the paradigms themselves and try to explore our underlying assumptions and definitions behind the logic of how we seek knowledge. Thus there is a feedback relationship between science and philosophy - and sometimes plenty of tension!

One of the tenets behind the scientific method is that any scientific hypothesis and resultant experimental design must be inherently falsifiable. Although falsifiability is not universally accepted, it is still the foundation of the majority of scientific experiments. Most scientists accept and work with this tenet, but it has its roots in philosophy and the deeper questions of truth and our access to it.

What is Falsifiability?

Falsifiability is the assertion that for any hypothesis to have credence, it must be inherently disprovable before it can become accepted as a scientific hypothesis or theory.

For example, someone might claim "the earth is younger than many scientists state, and in fact was created to appear as though it was older through deceptive fossils etc.” This is a claim that is unfalsifiable because it is a theory that can never be shown to be false. If you were to present such a person with fossils, geological data or arguments about the nature of compounds in the ozone, they could refute the argument by saying that your evidence was fabricated to appeared that way, and isn’t valid.

Importantly, falsifiability doesn’t mean that there are currently arguments against a theory, only that it is possible to imagine some kind of argument which would invalidate it. Falsifiability says nothing about an argument's inherent validity or correctness. It is only the minimum trait required of a claim that allows it to be engaged with in a scientific manner – a dividing line between what is considered science and what isn’t. Another important point is that falsifiability is not any claim that has yet to be proven true. After all, a conjecture that hasn’t been proven yet is just a hypothesis.

The idea is that no theory is completely correct , but if it can be shown both to be falsifiable and supported with evidence that shows it's true, it can be accepted as truth.

For example, Newton's Theory of Gravity was accepted as truth for centuries, because objects do not randomly float away from the earth. It appeared to fit the data obtained by experimentation and research , but was always subject to testing.

However, Einstein's theory makes falsifiable predictions that are different from predictions made by Newton's theory, for example concerning the precession of the orbit of Mercury, and gravitational lensing of light. In non-extreme situations Einstein's and Newton's theories make the same predictions, so they are both correct. But Einstein's theory holds true in a superset of the conditions in which Newton's theory holds, so according to the principle of Occam's Razor , Einstein's theory is preferred. On the other hand, Newtonian calculations are simpler, so Newton's theory is useful for almost any engineering project, including some space projects. But for GPS we need Einstein's theory. Scientists would not have arrived at either of these theories, or a compromise between both of them, without the use of testable, falsifiable experiments.

Popper saw falsifiability as a black and white definition; that if a theory is falsifiable, it is scientific , and if not, then it is unscientific. Whilst some "pure" sciences do adhere to this strict criterion, many fall somewhere between the two extremes, with pseudo-sciences falling at the extreme end of being unfalsifiable.

Pseudoscience

According to Popper, many branches of applied science, especially social science, are not truly scientific because they have no potential for falsification.

Anthropology and sociology, for example, often use case studies to observe people in their natural environment without actually testing any specific hypotheses or theories.

While such studies and ideas are not falsifiable, most would agree that they are scientific because they significantly advance human knowledge.

Popper had and still has his fair share of critics, and the question of how to demarcate legitimate scientific enquiry can get very convoluted. Some statements are logically falsifiable but not practically falsifiable – consider the famous example of “it will rain at this location in a million years' time.” You could absolutely conceive of a way to test this claim, but carrying it out is a different story.

Thus, falsifiability is not a simple black and white matter. The Raven Paradox shows the inherent danger of relying on falsifiability, because very few scientific experiments can measure all of the data, and necessarily rely upon generalization . Technologies change along with our aims and comprehension of the phenomena we study, and so the falsifiability criterion for good science is subject to shifting.

For many sciences, the idea of falsifiability is a useful tool for generating theories that are testable and realistic. Testability is a crucial starting point around which to design solid experiments that have a chance of telling us something useful about the phenomena in question. If a falsifiable theory is tested and the results are significant , then it can become accepted as a scientific truth.

The advantage of Popper's idea is that such truths can be falsified when more knowledge and resources are available. Even long accepted theories such as Gravity, Relativity and Evolution are increasingly challenged and adapted.

The major disadvantage of falsifiability is that it is very strict in its definitions and does not take into account the contributions of sciences that are observational and descriptive .

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Martyn Shuttleworth , Lyndsay T Wilson (Sep 21, 2008). Falsifiability. Retrieved Apr 13, 2024 from Explorable.com: https://explorable.com/falsifiability

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Scientific Hypothesis — Definition & Examples - Expii

Scientific hypothesis — definition & examples, explanations (3).

Scientific Predictions

A comic strip explaining what a hypothesis is, featuring a scientist and his mouse companion. The text reads: A hypothesis is a proposed prediction or explanation based on evidence. It is typically written in "If...then..." format. Because a hypothesis is the starting point for further explanation, it must be able to be tested via the scientific method. Once you have your hypothesis, the next thing is to test it, using—you guessed it—the scientific method. Just remember—it's not important if your hypothesis turns out to be right. The important thing is to learn more about your question!

Image source: By Sylvia Freeman

Related Lessons

How can i write a good hypothesis.

If you are doing a scientific experiment and you want to discover something cool, you need to begin with a strong working hypothesis. This will form the basis and conceptual framework for the rest of your experiment.

It should start with a good research question. You might base this on an observation of something in the natural world. Maybe you think that if a frog is given caffeine, then it could jump further. Your research question might be "Does caffeine influence the jumping activity of frogs?"

The image shows a frog sitting in front of a cup of coffee.

Image source: By Sarah Morgan

Once you have your research question, you can start to form a hypothesis (plural: hypotheses). Most hypotheses follow this structure:

"If woah––––––, then woah––––––will happen."

An example using this structure is "if a frog drinks an ounce of coffee, then the frog will be able to jump at least twice as far".

What Is Changing?

In the hypothesis, you want to include your dependent and independent variables . An independent variable is something that you control. In this case, it would be if you allow the frog to drink coffee or not. A dependent variable is a variable that is changed because of the independent variable . In this case, the distance that the frog jumps is the dependent variable.

Basically, if the independent variable changes, then the dependent variable will change because of it.

Sometimes, you can even follow the structure of "If woah––––––, then woah––––––will happen because woah––––––".

In this structure, you can give more details as to why you believe your idea is true. For example, "If a frog drinks an ounce of coffee, then the frog will be able to jump at least twice as far because the frog has more energy ".

You also want to remember that your hypothesis should be testable . For example, it would be really difficult to test "if red is the best color, then the most popular flavor of drink is peach".

Now that everything is set up, you're ready to begin hypothesis testing through experiments.

What Happens If You Can't Prove Your Hypothesis?

Not every scientific experiment is a success. If you can't prove your original idea, then you've still discovered that the null hypothesis is true. This is basically the opposite of your original prediction (which is also called the alternative hypothesis).

You wanted to prove that "if a frog drinks an ounce of coffee, then the frog will be able to jump at least twice as far". The opposite of that is "if a frog drinks an ounce of coffee, then the frog will not be able to jump at least twice as far", or "the frog will only jump a normal distance".

What Is an Example of a Hypothesis?

Scientists Matthew Meselson and Franklin Stahl hypothesized that DNA is the "transforming principle" in organisms . After finishing their experiments, they were able to confirm their hypothesis based on the data they collected.

Scientists retest and build on the hypotheses of other scientists using new research methods. For example, because of Meselson and Stahl's hypothesizing and experimentation, scientists now know lots about DNA and can do things like make genetically modified organisms .

(Video) The Scientific Method

by Teacher's Pet

The word hypothesis basically means a prediction about what will happen. It is an educated guess and the framework of an experiment. Once you think of one, you can design an experiment to test it. Remember, you cannot prove a hypothesis is true. You can only see if the data supports your claim or not.

Scientific vs. Unscientific Explanations

James Bakese is a middle aged man from a remote village in Soweto, South Africa. It was a joyous moment when he landed himself a job with a Research Company in Johannesburg. After three years however, he went back home sickly and having lost weight. Some of his relatives claimed that this was due to a curse and thus advised him to sacrifice a goat to the ancestors. On the other hand, the Christians concluded that he was demon possessed and thus all he needed was to take some time in prayer, followed by a deliverance service. However some instructed him to go for a medical check up to determine what the cause of his physical problems was. After being tested, it was discovered that he was HIV+ and was immediately placed on Anti Retroviral medication. The story is just a representation of how different phenomena is viewed and explained by different groups of people. Science in general terms (that is if taken as a means of getting human knowledge) may be defined as “a procedure for the invention and evaluation of hypotheses” (Kemerling, 2002) which can be utilized in explaining different phenomena. On the other hand, unscientific explanations are beliefs and “truths” that may be unchallengeable but have no supported assertions. This essay explains the differences between scientific and unscientific explanations.

Scientific Explanations

Scientific explanations give proposals that are tentative but that can be evaluated, modified and changed if newer evidence is found. Historically, explanation is related to causation or to explain a phenomenon and to identify its cause. However in modern times, the description of the concept of explanation has changed. This was especially because there were theories that claimed the existence of processes and entities that are unobservable (Kemerling, 2002). Many of the scientific explanations can be requested by means of ‘why’ questions even though the question is not directly framed that way. For example why does the moon turn silvery during the sun eclipse? One of the most useful models for the scientific explanation’s structure is that of deductive argument and its conclusion is that event that will be explained (Kemerling, 2002). The premises for the above argument are one, statements (factual) of circumstances and second, scientific hypotheses that are offered to link the circumstances to that outcome stated in the conclusion. The difference between any prediction and the explanation of the event is if it had taken place (Kemerling, 2002). This means that any conclusion of the argument is only true if all the premises are also true. It should be noted that the truth in the hypotheses (which captures the relationship between the circumstances and those events that are to be discussed) must remain questionable. This means that the explanation’s quality is determined by the degree to which the hypothesis is reliable (Kemerling, 2002).

In order to understand scientific explanation, one needs to distinguish between one, ‘ explanandum ’ which is the fact that needs to be explained and secondly, ‘ explanans ’ which is the hypothesis that helps in the explaining (Cohen 2005). If for example one says that “Mary has a common cold because she was rained on,” the explanandum is that ‘Mary has a common cold” while the “ explanans” is that “Mary was rained on”. The theory of explanation specifies the relationship between the explanandum and the explanans , which is commonly referred to as the explanatory relevance. Typically all scientific explanations give in detail the events of a certain kind. They must however be placed as hypotheses and can either be rejected or accepted after empirical evidence is given. Moreover, scientific explanations must be testable (Cohen, 2005).

A hypothesis is testable if at least some of the predictions that have been made based on that hypothesis can confirm or disconfirm that hypothesis (Cohen, 2005). It is not possible to collect all the evidence and as such, it is possible to encounter evidence that contradicts the hypothesis that has been carefully constructed. Of course, this means that not at any time will the hypothesis be 100% confirmed by the evidence but only in varying degrees. The methods of testing a hypothesis can be direct or indirect. Direct testing is when a scientist observes the event that is described by the hypothesis and it is possible for him or her to see how it relates to the event that has been predicted by the hypothesis (Cohen, 2005). On the other hand, indirect testing occurs when the event ( explanans ) cannot be observed directly, meaning that one must use other observations that are entailed by the explanans .

Scientific explanations can be evaluated using these three criteria. One, with all the factors remaining constant, a hypothesis at least should be compatible with another previously well established hypothesis (Cohen, 2005). A good hypothesis should also have predictive and explanatory power meaning that its observable consequences (that are deducible) are to be more. Finally, with everything else being equal, a hypothesis that is simpler is better than a complex one. This means that it should posit fewer entities, simpler mathematical equations and be more natural (Cohen, 2005).

With this in mind, how is one to conduct scientific investigation, or rather what is the procedure of conducting a scientific research? One, the problem should be stated meaning that one makes a statement of the phenomenon that will be investigated. Of course, this is very important as this is the focus as one continues with the method (Kemerling 2002). Secondly, preliminary hypothesis should be devised, which are the good guesses of what the results or the answers will be. Any guess that is made will be acceptable and thus should not be dismissed. These guesses should however, be guided by what the previous established theories have stated (Cohen, 2005). Thirdly, additional information should be sought. The researcher should look at the phenomenon and get as many facts as he can for this will help him or her gain more knowledge about the preliminary notions. Additional facts to the ones that were intended are also obtained at this point (Kemerling, 2002).The next step would be to test the consequences; meaning that the facts will have to be analysed to get the true picture on whether these consequences occur or not. The consequences of the theory are tested through a controlled experiment and if the hypothesis is right, then proper deductions will be the result but if not, one must move a step backwards and figure out another hypothesis. Finally, the hypothesis should be put into application; apply the new explanation to the problem (that had originally been created) to arrive at that explanation. This cannot be done once and should therefore be repeated twice and again until the satisfactory solution is arrived at (Kemerling, 2002). Experimentation is very important as it confirms a hypothesis, doing away with any likely alternatives and therefore scientists always device experiments but only within the context of theory that is well designed. This means that scientific explanations do not rely only on observations but sometimes one will need to go a step further and conduct an experiment (Kemerling, 2002).

Unscientific Explanations

Science can be defined as the human effort to understand the natural world and how that world works, which is done with observable physical evidence as the basis of that understanding. This is done through observation of natural phenomena and/or through experimentation. Therefore, anything that is to be explained scientifically must be based on either observation or experimentation (Railsback, 2009). In many cases, scientific explanations answer the question “why” and not only the question “what”. On the contrary unscientific explanations will only answer the ‘what’ and ‘how’ questions. They are also to do with common sense. For example, the explanation that “the moon becomes silvery during the sun eclipse” is not scientific for although this can be noticed through observation, it is a “common sense” explanation (Hauser, 2002). However if one states the reasons why it turns silvery, the statement would be scientific; for to know this, it would require keen observation and scientific theory to support it. However, not all ‘why’ questions may be scientific as they may require one to only use logic. For example if one asks a person, “Why have you come?” This does not require any scientific explanation. The other difference between scientific and unscientific explanations is that experiments are usually conducted to verify the cause or the state of phenomena for the former but not for the latter (Hauser, 2002).

This may require sophisticated technology and much time to conclude. On the other hand, unscientific explanations are based on observations that may not be proved true), beliefs (religious and superstitious) and tales (Hauser, 2002). One may claim that a sickly person has been cursed, though this may be true or false, the “cursing” process causing the sickness cannot be verified through observation or experiment. The demon possession is also unscientific since it falls short of the scientific requirements. Sometimes people have come to believe in some in some phenomena until they can describe them in a way that one may think they are scientific explanations (Railsback, 2009). When for example one states that boils are caused by taking some types of food, this may be seen as a fact until the scientific research and experiments are conducted to disapprove the believe and explain that boils are in fact caused by a special type of bacteria.

In conclusion, scientific explanations are different from unscientific explanations due to the fact that they are conducted and presented differently. While scientific explanation must be hypothesised and then proved, unscientific explanations are only beliefs that are untested.

Cohen, C. & Copi, M. (2005). Science and hypothesis . Web.

Hauser, H. (1992). Act, aim, and unscientific explanation . Web.

Kerling, G. (2002). Scientific explanations : The structure of explanations. Web.

Railsback. (2009). What is science ? Web.

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Scientific Hypothesis

Embarking on a scientific journey requires hypotheses that challenge, inspire, and guide your inquiries. The essence of any research, a well-framed hypothesis, serves as the compass that directs experiments and Thesis statement analysis. Dive into this comprehensive guide that unfolds a rich tapestry of scientific hypothesis statement examples, elucidates the steps to craft your own, and shares invaluable tips to ensure precision and relevance in your exploratory endeavors.

What is a good Scientific hypothesis statement example?

A good scientific hypothesis statement should be clear, concise, and testable. It should predict a cause-and-effect relationship between two or more variables. For instance: “If soil moisture levels decrease, then plant growth rates will also decrease.”

What is an example of a scientific hypothesis statement?

Consider a researcher studying the effects of sunlight on plant growth. The hypothesis might be: “If a plant is exposed to increased hours of sunlight, then it will grow taller than a plant that receives fewer hours of sunlight.” This Simple hypothesis sets a clear expectation (plant growth) based on a specific condition (hours of sunlight) and is easily testable through experimentation.

100 Scientific Statement Examples

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Scientific thesis statements serve as the backbone of research, setting forth clear and testable claims about phenomena. These assertions provide researchers with a focused direction and help them communicate their study’s core intent. Below are captivating examples spanning diverse scientific disciplines.

Ecology: Increased urbanization will lead to a decrease in biodiversity in metropolitan areas.
Genetics: Alterations in the BRCA1 gene increase susceptibility to breast cancer in women.
Astronomy: Planets located within the habitable zone of their star system are more likely to contain traces of water.
Chemistry: Increasing the temperature of a reaction will increase the rate at which that reaction occurs, up to a point.
Physics: In the absence of air resistance, all objects fall at the same rate irrespective of their mass.
Marine Biology: Coral bleaching events are directly correlated with rising sea temperatures.
Meteorology: The increase in global temperatures has accelerated the melting rate of polar ice caps.
Neuroscience: Chronic exposure to stress can lead to irreversible damage in the hippocampus of the brain.
Geology: Tectonic activity along the Pacific Ring of Fire will increase the likelihood of major earthquakes in the region.
Botany: Plants grown in higher concentrations of carbon dioxide will have faster photosynthesis rates.
Zoology: Animals that have more intricate mating dances have a higher likelihood of attracting a mate.
Microbiology: Bacterial resistance to antibiotics increases with the overuse of these medications.
Biochemistry: Enzymes lose their effectiveness when subjected to temperatures beyond their optimal range.
Psychology: Exposure to violent video games correlates with aggressive behavior in adolescents.
Anthropology: Ancient human migration patterns can be traced through the study of mitochondrial DNA.
Pharmacology: The introduction of Drug X will reduce symptoms of depression more effectively than currently prescribed antidepressants.
Climatology: An increase in greenhouse gas emissions directly correlates with rising global temperatures.
Paleontology: The mass extinction event at the end of the Cretaceous period was caused by a meteor impact.
Mathematics: Prime numbers greater than 2 are always odd numbers.
Biophysics: Cellular osmosis rates are influenced by the concentration gradient of solute molecules.
Ornithology: Birds that migrate longer distances have more streamlined body shapes to enhance aerodynamic efficiency.
Immunology: Vaccinating children against measles will drastically reduce the occurrence of the disease in the general population.
Nanotechnology: Nanoparticles can be effectively used to target and treat specific cancer cells.
Environmental Science: The increase in plastic waste in oceans is negatively impacting marine life.
Molecular Biology: The transcription rate of DNA into RNA is influenced by specific protein regulators.
Entomology: Insect species that undergo metamorphosis have a higher survival rate than those that don’t.
Genomics: Identifying specific gene markers can help predict susceptibility to Type 2 Diabetes.
Agronomy: Crop yields improve with the rotation of specific plant species.
Astrophysics: Black holes can be identified by observing the radiation emitted at their event horizon.
Material Science: The tensile strength of a metal increases with the addition of specific alloys.
Toxicology: Prolonged exposure to pollutant X increases the risk of respiratory diseases in urban dwellers.
Endocrinology: Hormone imbalances can lead to metabolic syndromes in mammals.
Space Science: The existence of exoplanets around binary star systems suggests diverse planetary formation processes.
Physiology: High-intensity interval training (HIIT) increases metabolic rates more significantly than steady-state cardio exercises.
Quantum Mechanics: Particles can display both wave-like and particle-like behavior under specific observational conditions.
Pedology: Soil health directly influences the nutritional quality of food crops grown in that soil.
Mycology: Fungi play a critical role in forest ecosystems by decomposing organic matter and forming symbiotic relationships with trees.
Virology: Viruses that mutate rapidly pose higher challenges for vaccine development.
Hydrology: Urban development and deforestation increase the risk of flash floods due to reduced soil absorption capacities.
Structural Biology: The 3D arrangement of proteins influences their functionality and interaction with other molecules.
Thermodynamics: An isolated system will always move towards a state of maximum entropy.
Arachnology: Spider silk’s tensile strength can rival that of steel when adjusted for thickness.
Paleobotany: The presence of certain ancient pollen types can indicate past climatic conditions of a region.
Oceanography: Ocean acidification is causing significant disruptions to marine food chains.
Spectroscopy: Molecules can be identified based on the absorption and emission spectra of light they produce.
Cytology: Cell division rates can be influenced by the surrounding micro-environment and external growth factors.
Ethology: Animal behaviors, such as nesting and migration, often correlate with seasonal changes.
Optics: Light’s behavior changes when passing through materials with different refractive indices.
Volcanology: Certain gas emissions from volcanoes can serve as early indicators of potential eruptions.
Bacteriology: Beneficial gut bacteria play a role in digestion and overall human health.
Nephrology: High sodium intake correlates with increased risk factors for chronic kidney diseases.
Chronobiology: The human circadian rhythm influences sleep patterns, alertness, and hormone production.
Rheology: The viscosity of a fluid changes under different temperatures and pressures.
Aerodynamics: Wing shapes in aircraft influence fuel efficiency and maneuverability.
Seismology: Earthquake aftershocks can be predicted based on the magnitude of the primary quake.
Mineralogy: Specific minerals can be identified by their unique crystalline structures and optical properties.
Pathology: The progression of disease Y is accelerated by genetic predisposition.
Cosmology: The observed redshift of distant galaxies supports the theory of the expanding universe.
Dermatology: UV exposure is the primary factor leading to premature skin aging.
Epidemiology: Vaccination rates correlate inversely with the incidence of infectious diseases in a population.
Gastroenterology: Diets high in processed sugars correlate with an increased risk of gastrointestinal disorders.
Forestry: Old growth forests store more carbon per acre than younger, reforested areas.
Astrobiology: The presence of methane on Mars might suggest microbial life below its surface.
Hematology: Individuals with blood type O are universal donors for blood transfusions.
Gerontology: Caloric restriction can extend lifespan in certain organisms.
Ichthyology: Overfishing in a specific region leads to a decline in the diversity of marine species.
Limnology: Freshwater lakes with high nutrient runoffs are more susceptible to algal blooms.
Mammalogy: The echolocation frequency of bats is adapted to their specific prey type.
Nuclear Physics: The stability of an atomic nucleus depends on the ratio of its protons to neutrons.
Odonatology: Dragonfly wing patterns play a significant role in mate selection and territorial disputes.
Petrology: The mineral composition of igneous rocks can indicate the conditions under which they formed.
Radiology: Modern MRI techniques can detect neural anomalies leading to specific cognitive disorders.
Statistical Physics: The behavior of macroscopic systems can be predicted by understanding the statistical behaviors of its microscopic constituents.
Urology: High fluid intake can reduce the risk of kidney stone formation.
Xenobiology: (Hypothetical) If life exists on exoplanets, it might not be carbon-based, leading to diverse biochemistries.
Zymology: The fermentation rate of yeast is influenced by sugar concentration and ambient temperature.
Dendrology: Tree ring patterns can serve as indicators of past climatic conditions.
Electrophysiology: Neuronal firing rates can be modulated by external electrical stimulation.
Fossil Fuels: The over-reliance on fossil fuels directly correlates with increased atmospheric CO2 levels.
Herpetology: Amphibian populations are declining globally due to a combination of habitat loss, pollution, and fungal diseases.
Kinesiology: Proper biomechanics during physical activities can reduce the risk of injury.
Lepidopterology: Moth species that mimic unpalatable butterfly species have higher survival rates against predators.
Mycorrhizae: Fungal and plant root symbiotic relationships enhance nutrient absorption.
Neuropharmacology: Drug Z shows potential in slowing the progression of Alzheimer’s disease.
Ornithological Behavior: Birds adjust their migratory patterns in response to changes in food availability.
Paleoecology: Fossilized pollen and spores can provide clues about ancient ecosystems and climate conditions.
Quantum Biology: Quantum effects might play a role in efficient energy transfer during photosynthesis.
Raptor Biology: Urban environments affect the hunting strategies of birds of prey.
Symbiosis: Mutualistic relationships between species X and Y lead to a more efficient nutrient cycle.
Tectonics: The movement of tectonic plates influences global climatic patterns over geologic time scales.
Vertebrate Zoology: The skeletal adaptations of burrowing animals provide increased strength and flexibility for underground movement.
Weather Patterns: La Niña conditions in the Pacific Ocean correlate with increased rainfall in the Southwestern United States.
X-ray Crystallography: Protein structures determined through X-ray diffraction techniques provide insights into molecular interactions and functionality.
Yeast Genetics: Manipulating specific genes in yeast can enhance their fermentation efficiency, impacting biofuel production.
Zoonotic Diseases: Human encroachment into wild habitats increases the risk of zoonotic disease transmission.
Agroforestry: Integrating trees into farmlands enhances biodiversity, improves soil quality, and can increase crop yields.
Bioinformatics: Computational tools in analyzing DNA sequences can predict potential functions of unknown genes.
Climatology: The ongoing rise in global average temperatures suggests a significant anthropogenic influence on the climate.
Dermatophytosis: Fungi causing skin infections in humans show increasing resistance to traditional antifungal treatments.
Ecotourism: Sustainable ecotourism practices can aid in conservation efforts and boost local economies.

Scientific Hypothesis Statement Examples for Research

Scientific hypothesis for research serve as tentative explanations for specific phenomena, which can be tested through experiments or observations. They’re foundational in guiding the direction of scientific inquiry.

Ozone Depletion: The depletion of ozone in Earth’s atmosphere is majorly contributed by human-made chemicals like CFCs.
Plant Growth: The rate of plant growth in a hydroponic system is faster compared to traditional soil gardening.
Aerodynamics: Modified wingtip designs reduce drag and improve fuel efficiency in aircraft.
Brain Plasticity: Regular cognitive exercises can slow the degenerative processes in aging brains.
Marine Biology: Coral reefs that experience frequent temperature fluctuations are more resilient to coral bleaching events.
Chemistry: The rate of chemical reaction X increases with a rise in temperature up to a certain point.
Geology: Regions with more frequent earthquakes have a thinner lithosphere.
Endocrinology: Consuming foods high in sugar leads to a rapid spike in insulin levels.
Environmental Science: Urban areas with more green spaces have lower levels of air pollution.
Quantum Mechanics: Particle behavior at the quantum level is influenced by the act of observation.

Scientific Investigation Hypothesis Statement Examples

Hypotheses in scientific investigations are proposed explanations or predictions that are directly testable, usually through experiments or special observational techniques.

Astronomy: The brightness variation of star X is due to the presence of a large exoplanet.
Microbiology: The presence of bacteria Y in water sources correlates with the onset of disease Z in communities.
Genetics: Gene A in fruit flies is responsible for wing color variation.
Neurology: The prolonged use of digital devices causes changes in the sleep patterns of adolescents.
Ecology: Introduction of a predator in ecosystem B will reduce the population of herbivores.
Physics: Materials with a higher rate of thermal conductivity cool down faster when exposed to the same conditions.
Psychology: Exposure to nature reduces levels of stress and anxiety in adults.
Volcanology: Active volcanoes with higher silica content in their magma are more likely to erupt explosively.
Anthropology: Early human migrations were influenced by climate changes.
Botany: Plants exposed to music grow faster than those that aren’t.

Scientific Null Hypothesis Statement Examples

Null hypothesis assert that there is no significant difference or effect, serving as a default stance in research until evidence suggests otherwise.

Medicine: Treatment A has no significant effect on the recovery rate of patients compared to the placebo.
Behavioral Science: There is no measurable difference in test scores between students taught with method X versus method Y.
Genetics: There is no relationship between gene B and the trait C in species D.
Climatology: Changes in global temperature do not depend on the amount of carbon dioxide in the atmosphere.
Pharmacology: Drug E does not significantly alter blood pressure levels more than the standard medication.
Zoology: There is no difference in the lifespans of species F in the wild versus in captivity.
Agriculture: Fertilizer G doesn’t increase crop yields more than the traditional fertilizer.
Physics: Changing the material of wire H does not affect its electrical conductivity.
Marine Science: The presence of pollutant I has no significant impact on fish reproduction rates.
Paleontology: The morphology of fossil J is not influenced by the environment it once inhabited.

Testable Scientific Hypothesis Statement Examples

A testable hypothesis is an actionable statement that can be examined and evaluated through empirical means, ensuring clarity and precision in scientific endeavors.

Meteorology: Increased cloud cover over region K results in decreased daytime temperatures.
Physiology: Regular exercise increases bone density in adults over the age of 50.
Geography: River meandering intensity is directly related to the gradient of the terrain.
Chemical Engineering: Catalyst L enhances the efficiency of reaction M by at least 20%.
Ornithology: Birds of species N change their migration patterns due to shifts in global temperature.
Material Science: Alloy O has twice the tensile strength of its primary metal component.
Sociology: Communities with more recreational areas report higher levels of general well-being.
Optics: Lens P refracts light at a different angle than lens Q, affecting image clarity.
Forensics: The presence of substance R is indicative of a specific cause of death.
Endocrinology: Hormone S levels are directly proportional to the intensity of emotion T.

Scientific Hypothesis Statement Examples for Action Research

In action research, hypotheses often focus on interventions and their outcomes, allowing for iterative improvements in practice based on findings.

Education: Implementing multimedia tools in classroom U enhances student engagement and understanding.
Urban Planning: Introducing green corridors in city V reduces the urban heat island effect.
Healthcare: Incorporating mindfulness exercises in daily routines reduces burnout rates among nurses.
Agriculture: Using natural predator W reduces pest populations without affecting crop health.
Community Development: Local art initiatives boost community morale and reduce vandalism rates.
Business: Employee training program X increases sales by at least 15% in the subsequent quarter.
Conservation: Implementing recycling program Y in city Z increases waste diversion by 30%.
Transportation: Carpool initiatives reduce traffic congestion during peak hours.
Mental Health: Cognitive-behavioral therapy techniques reduce symptom severity in patients with phobias.
Technology: Introduction of software A in company B enhances workflow efficiency by 25%.

Alternative Hypothesis Statement Examples in Scientific Study

The alternative hypothesis posits a potential relationship or effect, opposing the null hypothesis and indicating a significant result in research.

Oceanography: Deep-sea mining significantly affects the biodiversity of marine ecosystems.
Epidemiology: Vaccination rates are inversely related to the incidence of disease C in population D.
Astronomy: The luminosity of star E is influenced by the presence of nearby celestial bodies.
Toxicology: Exposure to chemical F at concentration G leads to health complications H.
Microbiology: The growth rate of bacteria I is inhibited by the presence of antibiotic J.
Hydrology: River K’s flow rate is influenced by the lunar cycle.
Seismology: Tectonic activity L is related to the occurrence of supermoons.
Anthropology: Cultural practices M in tribe N evolved due to environmental pressures O.
Quantum Physics: The behavior of particle P is determined by the presence of field Q.
Biochemistry: The activity of enzyme R is enhanced in the presence of compound S.

Scientific Development Hypothesis Statement Examples

These hypotheses address the developmental processes in various fields of science, focusing on growth, evolution, and stages of progression.

Embryology: Exposure to substance T during the embryonic stage leads to developmental anomalies in species U.
Evolution: Species V evolved specific traits in response to predation pressures.
Cognitive Science: Neural connections in the brain’s W region develop faster in children exposed to bilingual environments.
Plant Science: Plant X’s growth phases are influenced by light duration and intensity.
Endocrinology: The development of gland Y in adolescents is influenced by nutritional factors.
Neuroscience: Neuron type Z in the brain develops in response to sensory stimuli during early childhood.
Genetics: Certain genetic markers indicate a predisposition to developmental disorders A.
Palaeontology: Dinosaur species B developed feathers for thermoregulation before they were used for flight.
Pharmacology: The development of drug resistance in bacteria C is influenced by the misuse of antibiotics.
Sociology: Social structures D in ancient civilizations developed in response to geographic and climatic challenges.

What is a hypothesis in the scientific method?

A s cience hypothesis is a fundamental component of the scientific method, serving as a bridge between the formulation of research questions and the execution of experiments or observations. It is a proposed explanation or prediction about a specific phenomenon, based on prior knowledge, observation, or reasoning, which can be tested and either confirmed or refuted.

The role of a hypothesis in the scientific method can be broken down into several key points:

Foundation for Research: It provides a clear focus and direction for the research by stipulating what the researcher expects to find or verify.
Testability: For a hypothesis to be considered scientific, it must be testable through empirical methods (observations or experiments).
Falsifiability: A scientific hypothesis must also be falsifiable, meaning there should be potential outcomes of the research that would prove the hypothesis wrong. This is a critical aspect of the scientific method, ensuring that hypotheses are not merely speculative.
Predictive Power: A hypothesis often predicts specific outcomes, allowing for the design of experiments to test these predictions.
Refinement of Knowledge: Once a hypothesis is tested, it can either be supported, refuted, or require modification, contributing to the evolving body of scientific knowledge.

How do you write a hypothesis statement for Scientific Research – Step by Step Guide

Identify the Research Question: Before you can write a hypothesis, you need to pinpoint what you’re trying to find out. This could arise from observations, literature reviews, or gaps in current knowledge.
Conduct Preliminary Research: Get familiar with existing literature and studies on the topic to ensure your hypothesis is novel and relevant.
Determine the Variables: Identify the independent variable (what you will change) and the dependent variable (what you will observe or measure).
Formulate the Hypothesis: Write a clear, concise statement that predicts the relationship or effect between the variables. Ensure it’s testable and falsifiable.
Ensure Clarity: The hypothesis should be specific and unambiguous, so that anyone reading it understands your prediction.
Check Falsifiability: Ensure there are potential outcomes that could prove your hypothesis incorrect.
Re-evaluate and Refine: Go back to existing literature or seek peer feedback to ensure your hypothesis is sound and relevant.

Tips for Writing a Scientific Hypothesis Statement

Be Concise: A hypothesis should be a clear and concise statement, not a question or a vague idea.
Use Clear Language: Avoid jargon or overly complex language. The statement should be understandable to someone outside of the specific research field.
Ensure It’s Testable: A hypothesis should make a claim that can be supported or refuted through experimentation or observation.
Prioritize Falsifiability: While it might be tempting to craft a hypothesis that’s sure to be proven right, it’s essential that there are ways it could be proven wrong.
Avoid Absolutes: Steer clear of words like “always” or “never” as they can make your hypothesis untestable. Instead, opt for terms that indicate a relationship or effect.
Stay Relevant: Your hypothesis should be pertinent to the research question and reflect current scientific understanding.
Seek Feedback: Before finalizing your hypothesis, it can be beneficial to get feedback from peers, mentors, or experts in the field.
Be Prepared to Revise: As you delve deeper into your research, you may find that your original hypothesis needs refining or modification. This is a natural part of the scientific process.

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Dogmatic modes of science

Roy s. hessels.

Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands

Ignace T. C. Hooge

The scientific method has been characterised as consisting of two modes. On the one hand, there is the exploratory mode of science, where ideas are generated. On the other hand, one finds the confirmatory mode of science, where ideas are put to the test ( Tukey , 1980 ; Jaeger & Halliday , 1998 ). Various alternative labellings of this apparent dichotomy exist: data-driven versus hypothesis-driven, hypothesis-generating versus hypothesis-testing, or night science versus day science (e.g., Kell & Oliver , 2004 ; Yanai & Lercher , 2020 ). Regardless of the labelling, the dichotomy of an “idea-generating” versus an “idea-testing” mode seems pervasive in scientific thinking.

The two modes of science appear to be differentially appreciated. For example, exploratory research may carry the stink of “merely a fishing expedition” ( Kell & Oliver , 2004 ), or may be considered “weak” and yield unfavourable reviews (see the discussion of Platt [ 1964 ] in Jaeger & Halliday, 1998 ). Confirmatory research, on the other hand, seems to be considered as the holy grail in many areas of psychology (and vision science). Whether the appreciation for hypothesis-testing in psychology has been a reaction to the critique that theories in “soft areas of psychology” are “scientifically unimpressive and technologically worthless” ( Meehl, 1978 , p. 806) is an interesting question for debate. Nevertheless, the quintessential question in modern psychology is: “What is your hypothesis?” The correct answer one is expected to produce is a sentence at the level of a statistical analysis. Any other answer is wrong and yields the following response: “Ah, I see. You do exploratory research.” In Orwellian Newspeak “hypothesis” means “that which is to be decided on statistically” (cf. Yanai & Lercher , 2020 ), whereas “exploratory” means “descriptive” or even “unscientific.”

That the confirmatory mode of science is held in such high esteem is intriguing. Confirmation suggests that hypotheses or theories can be verified, a position diametrically opposed to that of, for example, Karl Popper, who claimed that theories can never be verified or confirmed, only refuted (e.g., Popper, 2002a ). Note that this cuts right into the heart of discussions on whether science can be inductive and rational or not ( Lakatos , 1978 ). It is not trivial semantics! But one does not find that a “refutatory” mode of science holds sway. Rather, refutation (or disconfirmation) is commonly avoided by the construction of ad-hoc auxiliary hypotheses when the data do not match with the theory (cf. the practice of Lakatosian defence, Meehl , 1990 ). Although sometimes frowned upon, ad-hoc hypotheses are not without merit. The observation of the planet Neptune by Galle in 1846 followed the ad-hoc hypothesis by Le Verrier and Adams (as discussed in Gershman , 2019 ): a great success for science. That ad-hoc hypotheses may also fail is evident from the hypothesised planet Vulcan by the same Le Verrier. That planet was never observed, although the discrepancy it addressed later proved to be relevant for Einstein’s theory of general relativity.

The discussion of confirmation versus refutation aside, the two-mode view of science is not merely a theoretical fancy that researchers debate about. It pervades increasingly more of the practicalities that researchers are faced with. The pre-registration movement, for example, seems to be built on this strict dichotomy. The Center for Open Science 1 writes that “Preregistration separates hypothesis-generating (exploratory) from hypothesis-testing (confirmatory) research” and this “planning improves the quality and transparency of your research.” Note the explicit normative statement here. But is a strict dichotomy of exploratory research (or data-driven or hypothesis-free) versus confirmatory research (or hypothesis-testing) sensible at all?

Is there such a thing as hypothesis-free exploration? Consider the case of a person sitting in their yard, peering at a pond through binoculars. Can we claim that this person is observing the world without hypotheses? According to Popper (2002b ), we cannot. He states: “observation is always observation in the light of theories” (p. 37). This need not be a formalised hypothesis according to the hypothetico-deductive method. Science is a human affair after all, it piggybacks on perception and cognition, which thrive through instinct, intuition, hunches, anticipation, quasi-informed guesses, and expertise (cf. Brunswik , 1955 ; Riegler , 2001 ; Chater et al. , 2018 ). Such proto-hypotheses ( Felin et al. , 2021 ) do not always lend themselves neatly to verbalisation or formalisation, or are fuzzy at best ( Rolfe , 1997 ). Thus, the decisions of where to sit, what direction to peer in, what binoculars to use, how long to wait are hardly hypothesis-free.

At the other extreme, one can ask whether hypothesis-testing is possible in the absence of exploration. Clearly, exploration in Tukey’s sense is crucial for forming a hypothesis in the first place: “Ideas come from previous exploration more often than from lightning strokes” (Tukey, 1980 , p. 23). However, devising a critical experiment to put a hypothesis to the test inevitably involves exploration. Exploration of where in the stimulus-space to measure, which parameters to use for signal processing, and so forth. This should strike a chord with the experimental scientist. Theoretically, one might be able to conceive of an experiment that can be considered as purely “hypothesis-testing.” Yet, at best it would be the hypothetical limit on a continuum between the exploratory and confirmatory modes of science.

Thus, a strict two-mode view of science is too simplistic. Nevertheless, the practical implications of such a view may be substantial, also to those who abstain from initiatives such as pre-registration. In our experience, the strict two-mode view of science permeates the thinking of e.g., institutional review boards, ethics committees, and local data archiving initiatives. The procedures derived from this strict two-mode thinking tend to take on a Kafkaesque atmosphere: The bureau of confirmatory science will see you now. It will be most pleased to guide you on your way to doing proper science.

We are happy to concede that scientific studies may be characterised as being of a more or less exploratory nature and that some studies may be characterised as clear attempts to refute or decide between scientific hypotheses. We also understand that some procedures taken up by institutional review boards, ethics committees, journals (pre-registration), and so forth, are meant to counter phenomena such as “HARKing” (the evil twin of the ad-hoc auxiliary hypothesis), “p-hacking”, blatant fraud, or to increase the replicability of science (e.g., Nosek et al. , 2012 ; Open Science Collaboration , 2015 ). Good intentions do not solely validate the means, however. What we vehemently oppose is the adoption and dogmatic use of a simplistic model of science and the scientific method that all research should adhere to. Dogma has no place in science, nor has it proved particularly effective throughout the history of science ( Feyerabend , 2010 ).

In our view, the dogmatic two-mode view of science obscures a deeper discussion—that of the goal or purpose of science. According to the influential paper by the Open Science Collaboration (2015 ) it is “that ultimate goal: truth” (p. 7). This contrasts starkly with a quote from Linschoten (1978 ):

The statement that science seeks truth is meaningless. The word “truth” either means too much or too little. It has no scientifically relatable meaning, unless truth is equivalent to relevant knowledge. Knowledge is relevant when it allows us to explain, predict, and control phenomena (p. 390). 2

If one considers hypotheses to be true or false, scientific findings to be true or false, and theories to be true or false, then a purely confirmatory way of thinking makes sense. All efforts to replicate—that is, to decide on which findings are really true—using the right statistical ( Gigerenzer , 2018 ) or methodological rituals ( Popper , 2002b ) will inevitably bring one closer to that truth. If one is less concerned with truth, and more with predicting tomorrow, then the exploratory versus confirmatory dichotomy is not all that relevant. One would rather have meaningful discussions about generalisability ( Yarkoni , 2020 ) or representativeness ( Brunswik , 1955 ; Holleman et al. , 2020 ). Anything that will yield a better prediction of tomorrow is useful, whether arrived at through Popper’s hypothetico-deductive methods, a hunch, a fishing trip, or counterinductively. According to Feyerabend ( 2010 , p. 1), “Science is an essentially anarchic enterprise: theoretical anarchism is more humanitarian and more likely to encourage progress than its law-and-order alternatives.” Science needs no dogma.

Acknowledgements

The authors thank Andrea van Doorn and Jan Koenderink for inspiring discussions and helpful comments.

1. https://www.cos.io/initiatives/prereg , accessed 14 May 2021.

2. In the original Dutch: “De uitspraak dat wetenschap waarheid zoekt, is zinloos. Het woord ‘waarheid’ betekent te veel of te weinig. Het geeft geen wetenschappelijk verbindbare betekenis, tenzij waarheid gelijkluidend is met relevante kennis. Kennis is relevant wanneer ze ons in staat stelt verschijnselen te verklaren, te voorspellen, en te beheersen.”

Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding: The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD: Roy S. Hessels https://orcid.org/0000-0002-4907-1067

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Meaning of unscientific in English

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Our random and unscientific sampling of local opinion in the main shopping area turned up relatively little hostility to the proposal .
An unscientific survey of our readers found that nearly 40% of them keep three sizes of clothes in their closets .
An unscientific online poll of hundreds of doctors said most wouldn't encourage their children to get into medicine .
She said that she had come to realize that people on the left are every bit as prone to being stupid and unscientific as right-wing people .
a posteriori
illogicality
illogically
inclusive disjunction
incoherently
non sequitur
non-theoretical
nonscientific
overintellectualization
teleological
theoretical
theoretically

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singing each musical note perfectly, at exactly the right pitch (= level)

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