If you are conducting research on a specific population, you will want to make sure that your sample of that population is representative. If your sample is representative of your population, you will be able to confidently generalize the results of your study to that population. But what exactly does that mean?
First, let’s review the difference between your population and your sample, as many students often get these terms confused. Your sample is the group of individuals who participate in your study. These are the individuals that provide the data for your study. Your population is the broader group of people that you are trying to generalize your results to. So, for example, if you wanted to determine the relationship between gratitude and job satisfaction in shark biologists, your sample might consist of 30-40 individual shark biologists. Your population might be “shark biologists in the United States,” or, if the scope of your study was more narrow, “shark biologists in Florida.”
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A representative sample is one that accurately represents, reflects, or “is like” your population. A representative sample should be an unbiased reflection of what the population is like. There are many ways to evaluate representativeness—gender, age, socioeconomic status, profession, education, chronic illness, even personality or pet ownership. It all depends on how detailed you want to get, the scope of your study, and what information about your population is available.
So, if most shark biologists in the population are women, but your sample is all male, you do not have a good case for representativeness because your sample does not share the same characteristics as the larger population. In this case, you cannot generalize the results of your study to the population (i.e., make a broader statement on shark biologists based on your results), because your sample has evidence of major differences from your population.
Lack of representativeness often comes from sampling errors or biases. An example of sampling error would be conducting a survey of how many people eat dairy products by recruiting participants from your local popular vegan café. Another example would be studying the drinking habits of college students, but only sampling from members of fraternities. In these examples, it is easy to see how the characteristics of the samples may potentially bias the results.
So, how do you avoid sampling error and select a representative sample? First, thoughtfully consider your sampling frame (your possible participants) and recruitment procedures. Avoid only recruiting members of a certain subset of your population, like the fraternity members or vegan café-goers in the above examples. Next, a good way to reduce bias in sampling is to randomly sample from your sample frame. Through this, you minimize any selection biases that might occur, such as volunteer bias. You also can implement a stratification protocol, such as proportionate stratified sampling. Let’s say you do your research and find out your population of shark biologists are 80% women. You could then make sure that 80% of your sample consists of women, such as by quota sampling. Another factor to consider is the size of your sample; larger samples will tend to be more representative (assuming you are conducting random sampling).
Finally, keep in mind that its unlikely that every sample will be perfectly similar to population of interest. There will always be a little sampling error associated with any study, unless you sample every single member of your population.