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.
Scientific Method Glossary PDF
Download and print the scientific method glossary. The comprehensive glossary is two pages, while the scientific method glossary for kids is one page:
Scientific Method Glossary for Kids
Observation
Using your senses to learn about the world around you. You might see, hear, smell, touch, or taste something interesting!
Question
A science question you ask based on your observation.
Example: What makes plants grow faster?
Hypothesis
A smart guess or prediction you can test.
Example: If a plant gets more sunlight, then it will grow faster.
Experiment
A test you do to see if your hypothesis is correct.
Variable
Something that can change in an experiment. There are three types:
- Independent Variable – The thing you change.
- Dependent Variable – The thing you measure.
- Control Variables – The things you keep the same.
Control Group
A group that does not get the change. You compare results with this group.
Data
Information you collect during your experiment—numbers, pictures, or notes.
Results
What happened in your experiment. These include your data and any patterns you see.
Conclusion
What you learned from your experiment. Did the results match your hypothesis?
Trial
One time you do the experiment. More trials = better results.
Evidence
Facts and data that help explain what happened in your experiment.
Graph
A picture (like a bar or line graph) that shows your data in an easy-to-read way.
Scientific Method
The step-by-step process scientists use to learn about the world by asking questions, testing ideas, and using evidence.
Comprehensive 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 sampling distribution of the 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 variable is a factor kept constant to ensure a fair test. 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 may be quantitative (numbers) or qualitative (descriptions).
Data Table: A structured table that organizes data from a science experiment, typically with columns for 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 or 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 refers to the difference between a measured value and the true value. It may result from limitations in instruments, conditions, or human error.
Experiment: An experiment is a repeatable procedure that tests a hypothesis by manipulating variables and observing outcomes.
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 visual representation of data, typically showing the relationship between variables. 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 testable prediction about the effect of the independent variable on the dependent variable.
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 assignment means randomly selecting whether a test subject will be in a treatment or control group.
Independent Variable: The independent variable is the variable the researcher changes or manipulates.
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 involves using data from a sample to draw conclusions about a population.
Internal Validity: Internal validity refers to the extent to which an experiment demonstrates a causal relationship between the independent and dependent variables.
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: The 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 null 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 p-value of less than 0.05 means there’s less than a 5% probability that the observed effect is due to chance. 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: Statistical power is the probability that a test will correctly reject a false null hypothesis. Higher power reduces the chance of Type II 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.
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 condition 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. It compares the difference between two group means relative to the variation in the data. 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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