The Relationship Between Population, Sample & Generalizability

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  • 0:07 Sampling
  • 1:33 Population and Sampling Frame
  • 2:58 Representativeness
  • 5:11 Generalizability
  • 6:33 Lesson Summary
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Lesson Transcript
Instructor: Natalie Boyd

Natalie is a teacher and holds an MA in English Education and is in progress on her PhD in psychology.

Researchers try their best to gather a sample that represents their population. But why is this important? In this lesson, we'll look at the relationship between population, sample, and generalizability in research.


Matt is a psychologist who is interested in studying how much children remember from watching a television show about science. He wants to know if this could be a good educational tool for schools to use to help support children in learning scientific concepts.

So, Matt decides to do a study: he'll show the television show to students and then give them a short science quiz to see if they remember what the show taught.

That seems pretty straightforward, but even before he begins the study, he's faced with questions: what students should he show the program to? First graders? Third graders? Children in city schools or rural schools?

Matt's questions center around his sample, or the group of subjects in a study. Since it's not practical for Matt to test every single kid in the world, he has to narrow it down a bit.

Sampling is the process whereby a researcher chooses the sample for his or her study. For example, Matt might choose to test only students at a particular school or only students of a particular age. Maybe he chooses his sample based on gender or favorite book. There are many ways he can choose his sample.

Let's look closer at the relationship between samples, populations, and generalizability of results.

Population & Sampling Frame

OK, so Matt wants to know how much children remember from the television show. If he's really interested in studying how much all children from around the world remember, that's a lot of children and, as we mentioned before, he has to narrow it down a bit.

The population of a study is the group of people that a researcher is interested in. Usually, the population is too large to actually measure. For example, Matt's population might be every child in the entire world.

Even if a population isn't as big as every child in the world, it can still be too big to study. For example, maybe Matt's not interested in every child in the world. Maybe he's just interested in every child in the school district where he works. That can still be a very large number of students!

To narrow it down, Matt has to choose who to study from a sampling frame, or group of people from which a sample is drawn. Maybe Matt chooses a specific elementary school to do his research at. His population might be the children in the entire district, and his sampling frame is all the children at the entire school.

He's still not going to study all of the students at the school; maybe he'll only choose half of the first-, second-, and third-graders. His sample is only a portion of his sampling frame, just like his sampling frame is just a portion of his population.


The point of Matt's study, just like the point of all studies, is for the things he observes in his sample to say something about the population at large. Matt wants to be able to say that all of the first-, second-, and third-grade students in the district will behave the same way that the ones in his sample do.

In that way, the main goal of sampling is to choose a sample that represents the population well. If Matt only chooses blue-eyed children to study, but most students in the district are brown-eyed, then his sample doesn't represent the population well.

There are three factors that influence the representativeness of a sample.

1. Sampling procedure. There are many different ways to choose a sample, and some are better than others. If Matt purposely chooses only the smartest kids in the school, his sample will not represent the population. But if he chooses kids by flipping a coin, he's more likely to get a representative sample.

2. Sample size. The larger a sample is, the more likely it is to represent the population. If Matt only chooses two kids, they probably aren't going to be like most of the kids in the district. But if he chooses two hundred kids, he's more likely to have a representative sample.

3. Participation rate. Imagine that Matt chooses two hundred kids, shows them the show, and then gives them all the science quiz. But what if only twenty kids actually take the quiz? The others all just throw the quiz in the trash. Then Matt's sample has gone from two hundred to twenty. Not only that, the students who are most likely to throw the quiz away are the ones who find science hard. So, now Matt's left with the kids who are generally good at science, and his sample is no longer representative.

In Matt's case, this might seem a little silly; after all, it's likely that most of the kids will actually take the quiz. But participation rates are of concern in many studies, particularly when it involves surveys. The people who respond to surveys are generally not representative of the entire population.

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