When we think of the term “population,” we usually think of people in our town, region, state or country and their respective characteristics such as gender, age, marital status, ethnic membership, religion and so forth. In statistics the term “population” takes on a slightly different meaning. The “population” in statistics includes all members of a defined group that we are studying or collecting information on for data driven decisions.
A part of the population is called a sample. The sample is a proportion of the population, a slice of it, a part of it and all its characteristics. A sample is a scientifically drawn group that actually possesses the same characteristics as the population – if it is a sample drawn randomly.(This may be hard for you to believe, but it is true!)
Randomly drawn samples must have two characteristics:
*Every person has an equal opportunity to be selected for your sample; and,
*Selection of one person is independent of the selection of another person in your sample.
What is great about random samples is that you can generalize to the population that you are interested in. So if you sample 500 households in your community, you can generalize to the 50,000 households that live there. Think of a sample as a slice of the whole pie. If you match some of the demographic characteristics of the 500 with the 50,000, you will see that they are surprisingly similar. That is the beauty of a sample!
Besides being representative of populations, samples are helpful time savers for doctoral students. Imagine if you had to use populations for your dissertation research!
Return from the difference between a population and a sample to samples and size.