When we carry out scientific experiments, it’s important that we follow specific methods to get the most accurate results possible. Without these methods it’s very easy to accidentally skew the results of our study without realizing it.
One of the most basic aspects of scientific methodology is carefully controlling variables. A variable is anything in an experiment that can change.
For example, let’s say we are investigating whether plants grow faster when treated with fertilizer. Some variables in this experiment might be the type of plant used, the room temperature, how much fertilizer the plants receive, the amount of sunlight, or how often the plants are watered.
In scientific experiments, it’s vitally important that we only change one variable. If we are investigating the effect of fertilizer on plants, the most logical variable to change would be the amount of fertilizer the plants are treated with. This is our independent variable, while all the other variables that stay the same are our controlled variables.
There is another type of variable in every experiment, and that is called the dependent variable. The dependent variable is what changes in response to the independent variable; in other words, it is dependent on the independent variable. In our plant example, the dependent variable is how much the plants grow in response to different amounts of fertilizer.
So why is it so important to only change one variable? If we change more than one variable, we won’t know if that variable or the independent variable is causing changes in the dependent variable. Extra, unwanted variables are called extraneous variables.
To illustrate with our plant example: Let’s say that we were going to treat three groups of pea plants with either low, medium, or high levels of fertilizer. If we changed the plants in the low fertilizer group to tomato plants instead, and those plants grew faster than the others, we wouldn’t know if the faster growth was because of the low fertilizer treatment or because tomato plants grow faster than pea plants.
Something else that needs to be added to our hypothetical plant experiment is a control group. A control group is a group isolated from the rest of the experiment, where nothing is done to change the dependent variable. In our plant example, the control group would be a set of pea plants that are not treated with any fertilizer.
We use control groups to make sure that the independent variable is actually what is causing changes in the dependent variable and not something else. For example, if all of our pea plants died and didn’t grow at all, the control group would allow us to conclude that this was because something was wrong with all the pea plants, not because fertilizer kills plants.
This kind of experiment, with variables and groups, is a great way to study the growth of pea plants, but might not work as well to study things like human cultures or complex behavior. In some circumstances, scientists use methods like interviews or participant observation to collect qualitative, or non-numerical, data. This is also a type of science, and can be used along with quantitative studies to help us learn more about the world.