hypothesis vocab word

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Definition of hypothesis noun from the Oxford Advanced American Dictionary

• formulate/advance a theory/hypothesis
• build/construct/create/develop a simple/theoretical/mathematical model
• develop/establish/provide/use a theoretical/conceptual framework/an algorithm
• advance/argue/develop the thesis that…
• explore an idea/a concept/a hypothesis
• make a prediction/an inference
• base a prediction/your calculations on something
• investigate/evaluate/accept/challenge/reject a theory/hypothesis/model
• design an experiment/a questionnaire/a study/a test
• do research/an experiment/an analysis
• make observations/calculations
• take/record measurements
• carry out/conduct/perform an experiment/a test/a longitudinal study/observations/clinical trials
• run an experiment/a simulation/clinical trials
• repeat an experiment/a test/an analysis
• replicate a study/the results/the findings
• observe/study/examine/investigate/assess a pattern/a process/a behavior
• fund/support the research/project/study
• seek/provide/get/secure funding for research
• collect/gather/extract data/information
• yield data/evidence/similar findings/the same results
• analyze/examine the data/soil samples/a specimen
• consider/compare/interpret the results/findings
• fit the data/model
• confirm/support/verify a prediction/a hypothesis/the results/the findings
• prove a conjecture/hypothesis/theorem
• draw/make/reach the same conclusions
• read/review the records/literature
• describe/report an experiment/a study
• present/publish/summarize the results/findings
• present/publish/read/review/cite a paper in a scientific journal

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• 2 [ uncountable ] guesses and ideas that are not based on certain knowledge synonym speculation It would be pointless to engage in hypothesis before we have the facts.

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Scientific Method Vocabulary Terms

The scientific method involves a hypothesis, variables, controls, experiments, and other concepts and terms that may be confusing. This is a glossary of key scientific method vocabulary terms and their definitions .

Glossary of Scientific Method Words

Anomaly: An anomaly is an observation that differs from expectation or from accepted scientific views. Anomalies lead scientists to revise a hypothesis or theory.

Central Limit Theorem:  The central limit theorem states that with a sufficiently large sample, the sample mean will be normally distributed. A normally distributed sample mean is necessary to apply the  t  test, so if you are planning to perform a statistical analysis of experimental data, it’s important to have a big sample.

Conclusion:  The conclusion is your determination of whether the hypothesis should be accepted or rejected. It is one of the steps of the scientific method.

Control Group :  The control group is the set of test subjects randomly assigned to  not  receive the experimental treatment. In other words, the independent variable is held constant for this group.

Control Variable :  A control is any variable that does not change during an experiment. It is also known as a  constant variable.

Correlation: A correlation is a relationship between two variables that can be used to predict the behavior or value of one variable if the other is known. Correlation is not the same as causality. In other words, correlating two variables doesn’t always imply one causes the other.

Data:  (singular: datum) Data refers to any facts, numbers, or values obtained in an experiment.

Data Table: This is a T-shaped diagram used to display data from a science experiment. It includes the values of the independent and dependent variables.

Dependent Variable:  The dependent variable is the variable that responds to the independent variable. It is the one that is measured in the experiment. It is also known as  the dependent measure ,  responding variable.

Double-blind :  When an experiment is double-blind, it means neither the researcher nor the subject knows whether the subject is receiving the treatment or a placebo. “Blinding” helps reduce biased results.

Empty Control Group:  An empty control group is a type of control group which does not receive any treatment, including a placebo.

Error : Error is a measure of the difference between a measured or calculated value and a true value.

Experiment : An experiment is a procedure that tests a hypothesis.

Experimental Group:  The experimental group is the set of test subjects randomly assigned to receive the experimental treatment.

Extraneous Variable:  Extraneous variables are extra variables (i.e., not the independent, dependent, or control variables) that may influence an experiment, but are not accounted for or measured or are beyond control. Examples may include factors you consider unimportant at the time of an experiment, such as the manufacturer of the glassware in a reaction or the color of paper used to make a paper airplane.

Fact: A fact is a statement based on evidence obtained from direct observation.

Graph: A graph is a picture that displays information. Examples of graphs include line graphs and bar graphs. The most common type of graph displays values of the independent and dependent variables.

Hypothesis:  A hypothesis is a prediction of whether the independent variable will have an effect on the dependent variable or a prediction of the nature of the effect.

Independence  or  Independently:  Independence means one factor does not exert influence on another. For example, what one study participant does should not influence what another participant does. They make decisions independently. Independence is critical for a meaningful statistical analysis.

Independent Random Assignment:  Independent random assignments means randomly selecting whether a test subject will be in a treatment or control group.

Independent Variable:  The independent variable is the variable that is manipulated or changed by the researcher. There is one independent variable in an experiment.

Independent Variable Levels: Independent variable levels refers to changing the independent variable from one value to another (e.g., different drug doses, different time duration). The different values are called “levels.”

Inferential Statistics:  Inferential statistics means applying statistics (math) to infer characteristics of a population based on a representative sample from the population.

Internal Validity:  An experiment is said to have internal validity if it can accurately determine whether the independent variable produces an effect.

Law : A scientific law is a generalization that describes what one expects to happen in a certain situation. For example, the law of gravity makes it possible to predict an object will fall if it is dropped. Laws can be used to predict behavior, but do not explain it.

Log Book: A log book or notebook records all of a scientist’s observations about an experiment. Entries are typically recorded in permanent ink.

Mean:  The mean is the average  calculated by adding up all the scores and then dividing by the number of scores.

Null Hypothesis :  Th null hypothesis is the “no difference” or “no effect” hypothesis, which predicts the treatment will not have an effect on the subject. The null hypothesis is easier to assess with a statistical analysis than other forms of a hypothesis.

Null Results (Nonsignificant Results):  If a researcher obtains nulls results, it means the results do not disprove the null hypothesis. Null results don’t  prove  the null hypothesis, because the results may have resulted from a lack of power. Some null results are type 2 errors.

Observation: An observation is information collected using one of the senses (sight, sound, touch, taste, scent).

p < 0.05:  This is an indication of how often chance alone could account for the effect of the experimental treatment. A value  p  < 0.05 means that 5 times out of a hundred, you could expect this difference between the two groups, purely by chance. Since the chance of the effect occurring by chance is so small, the researcher may conclude the experimental treatment did indeed have an effect. Note other  p  or probability values are possible. The 0.05 or 5% limit simply is a common benchmark of statistical significance.

Placebo (Placebo Treatment):  A placebo is a fake treatment that should have no effect, outside of the power of suggestion. Example: In drug trials, test patients may be given a pill containing the drug or a placebo, which resembles the drug (pill, injection, liquid) but doesn’t contain the active ingredient.

Placebo Effect : The placebo effect is a beneficial effect due to a subject’s belief in the power of the treatment. No active ingredient or other property of the placebo is responsible for the positive effect.

Population:  A population is the entire group the researcher is studying. If the researcher cannot gather data from the population, studying large random samples taken from the population may be used to estimate how the population would respond.

Power:  Power reflects the ability to observe differences or avoid making Type 2 errors .

Random   or Randomness:  To be random means to be selected or performed without following any pattern or method. To avoid unintentional bias, researchers often use random number generators or flip coins to make selections. (learn more)

Results:  The results are the explanation or interpretation of experimental data. This includes calculations made from the data.

Statistical Significance:  Statistical significance is the observation, based on the application of a statistical test, that a relationship probably is not due to pure chance. The probability is stated (e.g.,  p < 0.05) and the results are said to be  statistically significant .

Simple Experiment : A simple experiment is a basic experiment designed to assess whether there are a cause and effect relationship or test a prediction. A fundamental simple experiment may have only one test subject, compared with a controlled experiment, which has at least two groups.

Single-blind:  A single-blind conditions occurs when either the experimenter or subject is unaware whether the subject is getting the treatment or a placebo. Blinding the researcher helps prevent bias when the results are analyzed. Blinding the subject prevents the participant from having a biased reaction.

T-test:  The T-test is a common statistical data analysis applied to experimental data to test a hypothesis. The t-test computes the ratio between the difference between the group means and the standard error of the difference (a measure of the likelihood the group means could differ purely by chance). A rule of thumb is that the results are statistically significant if you observe a difference between the values that are three times larger than the standard error of the difference, but it’s best to look up the ratio required for significance on a  t  table.

Theory : A theory is a systematic explanation for phenomena, based on testing many hypotheses. Because they are evidence-based, theories are typically accepted by scientists, but they may be modified or discarded if new evidence is presented.

Type I Error (Type 1 error):  A type I error occurs when you reject the null hypothesis, but it was actually true. If you perform the t-test and set  p  < 0.05, there is less than a 5% chance you could make a Type I error by rejecting the hypothesis based on random fluctuations in the data.

Type II Error (Type 2 error):  A type II error occurs when you accept the null hypothesis, but it was actually false. The experimental conditions had an effect, but the researcher failed to find it statistically significant.

Further Study

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Definition of hypothesis – Learner’s Dictionary

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(Definition of hypothesis from the Cambridge Learner's Dictionary © Cambridge University Press)

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Phonetic Respelling: [ hahy- poth - uh -sis, hi- ]

Definition:.

n. a proposition taken for granted as a premise from which to reach a conclusion

I have a hypothesis for my science experiment.

More example sentences:

What is the hypothesis of Calvin's science fair project?

theory; proposition; supposition; guess

fact; assurance

How many syllables in hypothesis ?

4 syllables Play Syllables Game

Divide hypothesis into syllables:

hy-poth-e-sis

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Scientific Hypothesis, Model, Theory, and Law

Understanding the Difference Between Basic Scientific Terms

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

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

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

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

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

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

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

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

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

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

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

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

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Step 1. ask a question.

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

Step 2. Do some preliminary research

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

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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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).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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.

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McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved August 30, 2024, from https://www.scribbr.com/methodology/hypothesis/

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