Control Variables: The Key to a Fair Experiment
The Scientific Method and the Need for Control
Imagine you want to test if a new brand of fertilizer makes plants grow taller. You plant two seeds in identical pots with the same soil. You give one plant the new fertilizer (Plant A) and the other plant no fertilizer at all (Plant B). After a few weeks, Plant A is much taller. It seems like the fertilizer worked! But what if you had placed Plant A on a sunny windowsill and Plant B in a dark closet? Or what if you watered Plant A every day but forgot to water Plant B? In this case, you wouldn't know if the growth was due to the fertilizer, the sunlight, the water, or a combination of all three. Your experiment would be unfair.
This is where the concept of control comes in. The scientific method is built on the idea of testing one thing at a time. To do this fairly, we must identify and control all the other factors that could influence the outcome. These factors are called variables. A variable is anything that can change or be changed in an experiment. There are three main types of variables, and understanding their roles is the first step to good experimental design.
- Independent Variable (IV): This is the one factor that the investigator intentionally changes to see its effect. It is the presumed cause. In our example, the independent variable is the type of fertilizer (new fertilizer vs. no fertilizer).
- Dependent Variable (DV): This is what you measure in the experiment. It is the response, or the presumed effect that is caused by the independent variable. In our example, the dependent variable is the height of the plant.
- Control Variables (CVs): These are all the other factors that could potentially affect the outcome of the experiment. To ensure a fair test, these must be kept constant (the same) for all groups or trials. In our example, control variables include amount of sunlight, amount of water, type of soil, type of pot, temperature, and the type of seed.
Identifying Control Variables in Different Scenarios
Learning to spot potential control variables is a crucial skill. It requires thinking carefully about everything that could influence your dependent variable. Let's analyze a few common experiment ideas.
| Experiment Question | Independent Variable (IV) | Dependent Variable (DV) | Key Control Variables (CVs) |
|---|---|---|---|
| Does the angle of a ramp affect the speed of a toy car? | Angle of the ramp | Speed of the car (e.g., time to travel a fixed distance) | Same car, same ramp surface, same starting point on the ramp, same method of release (no push) |
| Which paper towel brand is most absorbent? | Brand of paper towel | Amount of water absorbed (e.g., measured in grams) | Size of towel sheet, temperature of water, time submerged, method of dripping |
| Does listening to music improve test scores? | Presence of music (music vs. silence) | Score on a standardized test | Same test difficulty, same testing environment (room, lighting), same time of day, same student group |
Control Groups vs. Control Variables: A Critical Distinction
It's easy to confuse "control variables" with a "control group," but they are different concepts that work together. A control group is a baseline group in an experiment that does not receive the treatment or manipulation of the independent variable. It is used for comparison. Control variables, on the other hand, are the factors kept constant across all groups, including the control group.
Let's return to the fertilizer experiment. To do it correctly, you would set up two groups:
- Experimental Group: This group receives the treatment. (Plant A gets the new fertilizer).
- Control Group: This group does not receive the treatment. (Plant B gets no fertilizer).
Now, the control variables come into play: both plants must receive the same amount of sunlight, water, soil, etc. The only difference between the two groups should be the independent variable (the fertilizer). This way, if the experimental group grows taller, you can confidently attribute the difference to the fertilizer. The control group provides a reference point for what normal growth looks like under the controlled conditions.
A Practical Example: The Baking Soda and Vinegar Volcano
A classic science fair project is the baking soda and vinegar volcano. The goal is to see how the amount of baking soda affects the volume of the eruption. Let's design this experiment with proper controls.
1. Ask the Question: How does the amount of baking soda affect the volume of foam produced in a vinegar reaction?
2. Identify the Variables:
- Independent Variable (IV): Mass of baking soda. For example, you might test 10 g, 20 g, and 30 g.
- Dependent Variable (DV): Volume of foam produced. This could be measured by pouring the mixture into a graduated cylinder and seeing how high the foam rises.
- Control Variables (CVs): These are all the other factors that must stay the same for every trial to make it a fair test.
- Volume of vinegar: Always use 100 mL.
- Concentration of vinegar: Use the same bottle of vinegar (e.g., 5% acetic acid).
- Temperature: Perform all trials at room temperature.
- Mixing container: Use the same container (e.g., a 500 mL beaker) for every reaction.
- Mixing procedure: Add the baking soda to the vinegar in the same way each time (e.g., pour quickly from a small cup).
By controlling these variables, you ensure that any difference in the foam volume is due to the change in the amount of baking soda and not because you used more vinegar or a different container. This makes your results valid (you are measuring what you intend to measure) and reliable (the experiment can be repeated by others with the same results).
Common Mistakes and Important Questions
A: In the context of an experiment, they are essentially the same thing. A control variable is a factor that is held constant. Sometimes, "constant" is used to describe a factor that is naturally unchanging in the experiment (like the gravitational constant, g), while "control variable" refers to a factor the experimenter must actively work to keep constant (like temperature). For most school-level experiments, the terms can be used interchangeably.
A: If an important variable is not controlled, it becomes what is known as a confounding variable. This is an "extra" variable that you didn't account for that can mess up your results. In the plant experiment, if you don't control light, light becomes a confounding variable. If your fertilized plant gets more light, you can't tell if its growth is from the fertilizer or the light. Your experiment loses its validity, and any conclusions you draw become uncertain or wrong.
A: In practice, no. It's impossible to control everything. For example, tiny variations in air pressure or the exact genetic makeup of two seeds are very difficult to control. The goal is to identify and control the variables that are most likely to have a significant effect on the outcome. Scientists also use techniques like large sample sizes and randomization to minimize the effects of variables they cannot control.
Conclusion
Footnote
1 DV (Dependent Variable): The variable that is measured in an experiment. It 'depends' on the changes made to the independent variable.
2 IV (Independent Variable): The variable that is deliberately changed by the investigator in an experiment to observe its effect on the dependent variable.
3 CV (Control Variable): Any variable that is held constant throughout an experiment to prevent it from influencing the outcome.
4 Confounding Variable: An extraneous variable that is not controlled for and that unintentionally influences both the independent and dependent variables, creating a false association.