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.
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.