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The word “random” has a precise meaning in statistics.Random selection doesn’t just mean you can just randomly pick a few items to make up a sample.
You choose every 50th student from a list (a random selection method called systematic sampling) to create a sample of 50 students to study.
Example of non random selection: From the same list of 5,000 students, you randomly circle 50 names.
For example, in a psychology experiment, participants might be assigned to either a control group or an experimental group.
Some experiments might only have one experimental group while others may have several treatment variations.
The first 25 balls you draw go into the experimental group. Example of non-random assignment: you have a list of 50 people to assign to control groups and experimental groups.
You use your knowledge and experience to choose 25 people who you think would be better suited to the experimental group (a method called purposive sampling).
That method is actually something called , where you try to create a random sample by haphazardly choosing items in order to try and recreate true randomness.
That doesn’t usually work (because of something called selection bias).
Random selection means to create your study sample randomly, by chance.
Random selection results in a representative sample; you can make generalizations and predictions about a population’s behavior based on your sample as long as you have used a probability sampling method.