By Benjamin Twist • Aug 12, 2023 12:15 am EST
The purpose of a scientific experiment is to establish clear cause-and-effect relationships between variables. Variables that can change during an experiment—such as water temperature—are called scientific variables. Variables that remain constant—such as the local acceleration due to gravity—are known as constants.
The scientific method relies on three core types of variables: constants, independent variables, and dependent variables. Each type plays a distinct role in defining how a system behaves under controlled conditions.
Constants are values that must not change during an experiment or between experimental runs. Classic examples include the speed of light and the atomic weight of gold. In many practical experiments, properties that can vary under extreme conditions—like the boiling point of water at different altitudes—are treated as constants when the experiment is confined to a single location.
By deliberately holding these variables steady, researchers can isolate the true effect of the independent variable on the dependent variable. If extraneous variables are not constrained, they become confounding variables and can obscure the experiment’s conclusions.
TL;DR: Keep constants fixed to prevent confounding influences.
For example, in a study of plant growth versus sunlight exposure, the experimenter would control water volume, soil type, plant species, and planting time. Only the amount of light would vary, allowing a direct assessment of its impact on growth.
The independent variable is the factor the researcher deliberately changes to observe its effect. A well‑designed experiment changes only one independent variable at a time, ensuring that any observed changes in the outcome can be attributed to it. For instance, to determine how quickly water boils, one should vary either the heating temperature or the volume of water, but not both simultaneously.
The dependent variable, sometimes called the responding variable, is what the researcher measures to assess the impact of the independent variable. While an experiment can include multiple dependent variables, focusing on a single one clarifies the relationship. An example is measuring the amount of sugar that dissolves in a fixed volume of water at different temperatures: temperature is the independent variable, and dissolved sugar quantity is the dependent variable.
In some designs, researchers include a control group that is not exposed to the independent variable. The control group establishes a baseline against which experimental results are compared. In medical trials, for instance, several groups might receive varying doses of a drug while a control group receives no drug at all, allowing researchers to attribute any therapeutic effects to the medication itself.
Accurate data representation is essential for communicating findings. The independent variable is plotted on the horizontal axis (x‑axis), while the dependent variable occupies the vertical axis (y‑axis). Clear labeling, appropriate scales, and statistical markers help ensure that results are interpretable and credible.
