How does random selection differ from random assignment? Random selection refers to how the sample is drawn from the population as a whole, while random assignment refers to how the participants are then assigned to either the experimental or control groups.
It is possible to have both random selection and random assignment in an experiment. Imagine that you use random selection to draw 500 people from a population to participate in your study.
You then use random assignment to assign 250 of your participants to a control group (the group that does not receive the treatment or independent variable) and you assign 250 of the participants to the experimental group (the group that receives the treatment or independent variable). Why do researchers utilize random selection? The purpose is to increase the generalizability of the results.
By drawing a random sample from a larger population, the goal is that the sample will be representative of the larger group and less likely to be subject to bias.
Imagine that a researcher is selecting people to participate in a study. In order to pick participants, they might choose people using a technique that is the statistical equivalent of a coin toss.
They might begin by using random selection to pick geographic regions from which to draw participants. They might then use the same selection process to pick cities, neighborhoods, households, age ranges, and individual participants.
Another important thing to remember is that larger samples tend to be more representative because even random selection can lead to a biased or limited sample if the sample size is small.1
When the sample size is small, an unusual participant can have an undue influence over the sample as a whole. Using a much larger sample size tends to dilute the effects of unusual participants and prevent them from skewing the results.
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