A good way to learn about experiments is to look at bad ones. Examine the following bad experiment. Suppose we have created a new drug, a sleeping pill. We call it Nox-Out©. Now, we are interested in marketing it by saying that it is better at putting people to sleep than other sleeping pills. So, we decide to conduct an experiment.
First, we need some terminology. We already know that the control group is the group that does not get the procedure, treatment, or substance. In the Nox-Out© case the dose of Nox-Out© is the substance, here, the independent variable. So, now we can say that the control group does not get the independent variable. For contrast, the experimental group does get the independent variable. Finally, the dependent variable is how we measure the effects of the independent variable. Both groups will be measured in terms of the dependent variable. In this experiment, a good dependent variable might be hours of sleep after taking Nox-Out©.
Now comes the first bad experiment. Suppose the subjects in the control group are all between 60 and 80 years of age, and the subjects in the experimental group are all between 20 and 30 years of age. When we measure both groups using the dependent variable of hours of sleep, we may find that the young group sleeps longer. But, did that difference in the dependent variable come from the independent variable or from the age difference? Here, we cannot tell. When two or more factors contribute to a dependent variable difference, it is called a confound. Confounds are always a problem in experimentation, and they are the main reason that the study of experimental design is important.
Here is another bad experiment. Now let's house the control group in the new Hilton Hotel, and let's house the experimental group in the El Sleazo Motel, the one with the neon light that flashes all night. Now our dependent variable difference may show that Nox-Out© does not work very well. Again, the problem is one of experimental design. Subjects just find it more difficult to sleep at the El Sleazo, even when they have taken Nox-Out©. This example, too, is a confound.
A final bad experiment could be one in which we give Nox-Out© to the experimental group at 10:00 a.m. and then put them to bed; we put the control group to bed at 11:00 p.m. Again, the dependent-variable differences will lead us to believe that Nox-Out© does not work very effectively.
The message here is pretty obvious. Good experiments should differetiate the experimental and control groups only by giving or not giving the independent variable. So, to properly test Nox-Out©, both groups should be of the same age, sex, and degree of sleepiness. Also, they should be tested in the same environment (e.g.,. the Hilton).
When all of these details are taken care of, and when a difference in the dependent variable exists, then the experimenter can say it was the independent variable that caused the difference. The ability to make such statements is the main benefit of the experimental method. However, just because a study is an experiment does not give it that power. There have been plenty of bad experiments like those described above. So, a sophisticated student will look beyond the dependent variable to the design and the conduct of the experiment in order to determine the validity of the conclusion drawn from the data.