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Experimental Design in Statistics

So you want to find out height makes a difference in the frequency of dating? Or maybe you want to find out if that hard-core diet (which probably doesn’t include donuts) really makes a difference? In either case, you are going to need statistics.

Yay!

In statistics, there are two types of research: correlational and experimental. The type of research you use determines the type of answer you’ll get for your problem.

Correlational Designs

Our dating example is correlational research. In this type of research, you look at the changes in one variable as another variable changes without imposing a change.

In our dating example, you would want to know how the frequency of dating changes as height in inches changes. When we look at the data, we may see that as the height increases, so does the approximate number of dates. There may be something about tall people. But, as with all correlational research…

WE CANNOT ASSUME THAT ONE IS THE CAUSE OF THE OTHER

Since we’re measuring height and dating simultaneously, there is no way to logically state that being tall will get you more dates. It could also be that more dates make you taller. We know that there is a relationship there, but it is not clear what the CAUSE of the relationship is, whatever it may be. Given that we can’t determine the cause, it is not a true experimental design.

experimental design statistics
Comic by Randall Munroe

Experimental Designs

Our second example, the effectiveness of the hard-core diet, is fertile ground for a causal research – otherwise called experimental design in statistics.

Say you’re a personal trainer with a bunch of clients that are serious about fitness and weight loss. You have a pretty fantastic workout, but you want to test out a new diet plan. So, you give one group a new hard-core diet to go with their workout. The other group only has your awesome workout.

After a few months you tally the average weight loss for each group and notice that the hard-core diet group lost more weight. Based on this setup, we can say logically that the change in diet CAUSED the difference in weight loss. Since you kept basically everything except the diet the same, the only logical explanation for the difference in the weight loss is the only thing that was different between the two groups—the hard-core diet.

This is an experimental design because we are statistically determining whether a change in one variable, called a treatment, causes an effect in the other variable, sometimes called the effect. Unlike correlational variables, which occur simultaneously, in causal experimental designs, one variable occurs before the other and (drum roll) causes the other to change.

There are numerous ways to set up an experiment: Some methods involve randomly separating participants into a group that gets changed, the treatment group, and one that does not, the control group. Other methods involve randomly selecting a pre-existing group to receive a treatment and controls—this is called quasi-experimental design.

In every case, the kicker for experimental design in statistics is that there must be at least two groups that are the same in every respect, but one group gets a change so that the researcher can compare two, potentially different, outcomes.

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