Controlled variables
01 // Why they matter
A controlled variable is anything besides the independent variable that could affect the dependent variable, so you deliberately hold it constant across every trial. Controlled variables are what make a test fair: if only one thing is different between your trial groups, then any difference in the result can be blamed on that one thing.
Leaving a variable uncontrolled creates a confound — a second explanation for your result that you can no longer rule out. If two plant pots get different amounts of fertilizer but also sit in different windows, you cannot tell whether fertilizer or sunlight caused a height difference.
02 // Worked example: osmosis egg lab
In the osmosis egg lab, the independent variable is the soaking liquid (water or corn syrup). Everything else has to match between the two eggs.
| Starting egg size | similar mass before soaking |
| Volume of liquid | 250 mL for every egg |
| Temperature | room temperature for both |
| Soak time | 24 hours for both |
Formula A controlled variable is anything held the same on purpose so it cannot explain the result.
03 // When a variable is hard to control
- You cannot control every possible variable. List the ones most likely to affect your specific dependent variable, and control those first.
- If a variable truly cannot be controlled (like outdoor temperature in a field study), record it instead, so you can check afterward whether it lines up with an odd result.
- A control group is a different tool from a controlled variable — a control group is a whole trial that gets no treatment, used for comparison. See control group vs experimental group.
04 // Questions people ask
What is the difference between a controlled variable and a control group?
A controlled variable is a condition (like temperature or soak time) that is held the same across every trial. A control group is an entire trial or group that receives no treatment at all, used as a baseline to compare against.
How many controlled variables should an experiment have?
There is no fixed number — list every factor besides your independent variable that could plausibly change your dependent variable, and control as many of those as you reasonably can.
What happens if I forget to control a variable?
Your result has a confound: a second possible explanation you cannot rule out. The experiment can still run, but the conclusion becomes weaker, because you cannot say for certain which variable caused the change.