Systematic Random Samples

Systematic Random Samples

Author: Jonathan Osters

This lesson will explain systematic random sampling.

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Introduction to Statistics

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Source: Stick figures and sample created by Jonathan Osters

Video Transcription

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This tutorial is going to cover systematic sampling. Now, systematic sampling, by definition, is not inherently random. So, you have to be really careful about this. A systematic sample is different than a simple random sample in that for a systematic sample, what we call every k-th individual is chosen. And that value of k can be anything. We could choose every second individual, in which case all these green people are in, and all these black stick figures are out. Or we could do every third person, where one person is in and then skip two. And then fourth person is in and skip two. And then the seventh person, et cetera. Or we could go every fourth person.

Now, often people prefer systematic samples to simple random sample because systematic samples are so much easier to take. It's easier than getting a whole list of people and putting everyone's-- assigning everyone a name, or putting all the people's names in a hat. It's easier to just take every fifth person or whatever. Now , the trick is, can they be treated as simple random samples? And the question is sometimes. So, we'll talk about that in a minute.

But systematic sample, the nice thing about it is that it can be tailored to fit your sample size. If, suppose, you wanted a sample of 25 from 500 individuals. You could sample every 20th person, and you would obtain your sample of 25.

And it can be random. it is not inherently random, but it can be random. So long as the individuals are placed in order before the sampling begins. So, for instance, suppose that you have these 20 students in a class, and they're in rows. And suppose they were assigned to their desks randomly. If that were the case, the teacher could count off every fourth student and have five students go up to the chalkboard to do a homework problem on the chalkboard or something. So, person one, two, and three don't have to do it. And then person number four does. Five six and seven don't have to do it. And number eight does, et cetera, et cetera.

Now, the problem doing it this way is if they weren't randomly assigned then by selecting one person, you automatically know who all the rest of the people are going to be. And this gets to be a problem. So, Adamson was selected. Because Adler is right next to Adamson, you know that Adler won't get chosen. Nor will Anderson or Bueller, but Frye will. And so once you pick one person, all the rest of the people are predetermined. And if these people were randomly assigned to the seats, you wouldn't necessarily know just by picking Adamson who all the other people were going to be. But, in this case you would.

So, to recap, a simple random sample is the ideal sampling method. However, a systematic sample can be similarly valid. And it's so much easier to perform. It involves taking every k-th individual, but the population does need to be randomly sorted before the systematic selection. Otherwise, it won't be considered random. And systematic sample is the only new term that we had this time, but we also talked about simple random samples. Good luck. And we'll see you next time.

  • Systematic Random Sample

    A sampling method where every "k"th individual is selected for the sample (e.g. every 2nd, 4th, 20th individual)