Struggling with independent and dependent variables? This is the guide for you! We go over the definitions of independent and dependent variables so you can clearly understand the differences between the two, give examples of the variables in actual experiments, explain how to properly graph them, and end with one other important variable you need to know.
What Is an Independent Variable? What Is a Dependent Variable?
Before we discuss anything else, let’s first go over what variables are, the independent variable definition, and the dependent variable definition.
A variable is something you’re trying to measure. It can be practically anything, such as objects, amounts of time, feelings, events, or ideas. If you’re studying how people feel about different television shows, the variables in that experiment are television shows and feelings. If you’re studying how different types of fertilizer affect how tall plants grow, the variables are type of fertilizer and plant height.
There are two key variables in every experiment: the independent variable and the dependent variable. The independent variable is the variable whose change isn’t affected by any other variable in the experiment. Either the scientist has to change the independent variable herself or it changes on its own; nothing else in the experiment affects or changes it. Two examples of common independent variables are age and time. There’s nothing you or anything else can do to speed up or slow down time or increase or decrease age. They’re independent of everything else.
The dependent variable is what is being studied and measured in the experiment. It’s what changes as a result of the changes to the independent variable. An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age).
An easy way to think of independent and dependent variables is, when you’re conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that. You can also think of the independent variable as the cause and the dependent variable as the effect.
It can be a lot easier to understand the differences between these two variables with examples, so let’s look at some sample experiments below.
Examples of Independent and Dependent Variables in Experiments
Below are overviews of three experiments, each with their independent and dependent variables identified.
Experiment 1: You want to figure out which brand of microwave popcorn pops the most kernels so you can get the most value for your money. You test different brands of popcorn to see which bag pops the most popcorn kernels.
- Independent Variable: Brand of popcorn bag (It’s the independent variable because you are actually deciding the popcorn bag brands)
- Dependent Variable: Number of kernels popped (This is the dependent variable because it's what you measure for each popcorn brand)
Experiment 2: You want to see which type of fertilizer helps plants grow fastest, so you add a different brand of fertilizer to each plant and see how tall they grow.
- Independent Variable: Type of fertilizer given to the plant
- Dependent Variable: Plant height
Experiment 3: You’re interested in how rising sea temperatures impact algae life, so you design an experiment that measures the number of algae in a sample of water taken from a specific ocean site under varying temperatures.
- Independent Variable: Ocean temperature
- Dependent Variable: The number of algae in the sample
For each of the independent variables above, it’s clear that they can’t be changed by other variables in the experiment. You have to be the one to change the popcorn and fertilizer brands in Experiments 1 and 2, and the ocean temperature in Experiment 3 cannot be significantly changed by other factors. Changes to each of these independent variables cause the dependent variables to change in the experiments.
Where Do You Put Independent and Dependent Variables on Graphs?
Independent and dependent variables always go on the same places in a graph. This makes it easy for you to quickly see which variable is independent and which is dependent when looking at a graph or chart. The independent variable always goes on the x-axis, or the horizontal axis. The dependent variable goes on the y-axis, or vertical axis.
Here’s an example:
As you can see, this is a graph showing how the number of hours a student studies affects the score she got on an exam. From the graph, it looks like studying up to six hours helped her raise her score, but as she studied more than that her score dropped slightly.
The amount of time studied is the independent variable, because it’s what she changed, so it’s on the x-axis. The score she got on the exam is the dependent variable, because it’s what changed as a result of the independent variable, and it’s on the y-axis. It’s common to put the units in parentheses next to the axis titles, which this graph does.
There are different ways to title a graph, but a common way is “[Independent Variable] vs. [Dependent Variable]” like this graph. Using a standard title like that also makes it easy for others to see what your independent and dependent variables are.
Are There Other Important Variables to Know?
Independent and dependent variables are the two most important variables to know and understand when conducting or studying an experiment, but there is one other type of variable that you should be aware of: constant variables.
Constant variables (also known as “constants”) are simple to understand: they’re what stay the same during the experiment. Most experiments usually only have one independent variable and one dependent variable, but they will all have multiple constant variables.
For example, in Experiment 2 above, some of the constant variables would be the type of plant being grown, the amount of fertilizer each plant is given, the amount of water each plant is given, when each plant is given fertilizer and water, the amount of sunlight the plants receive, the size of the container each plant is grown in, and more. The scientist is changing the type of fertilizer each plant gets which in turn changes how much each plant grows, but every other part of the experiment stays the same.
In experiments, you have to test one independent variable at a time in order to accurately understand how it impacts the dependent variable. Constant variables are important because they ensure that the dependent variable is changing because, and only because, of the independent variable so you can accurately measure the relationship between the dependent and independent variables.
If you didn’t have any constant variables, you wouldn’t be able to tell if the independent variable was what was really affecting the dependent variable. For example, in the example above, if there were no constants and you used different amounts of water, different types of plants, different amounts of fertilizer and put the plants in windows that got different amounts of sun, you wouldn’t be able to say how fertilizer type affected plant growth because there would be so many other factors potentially affecting how the plants grew.
Summary: Independent vs Dependent Variable
Knowing the independent variable definition and dependent variable definition is key to understanding how experiments work. The independent variable is what you change, and the dependent variable is what changes as a result of that. You can also think of the independent variable as the cause and the dependent variable as the effect.
When graphing these variables, the independent variable should go on the x-axis (the horizontal axis), and the dependent variable goes on the y-axis (vertical axis).
Constant variables are also important to understand. They are what stay the same throughout the experiment so you can accurately measure the impact of the independent variable on the dependent variable.
Independent and dependent variables are commonly taught in high school science classes. Read our guide to learn which science classes high school students should be taking.
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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.