Why Is Sampling Important?

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Sampling, in statistics, is a method of answering questions that deal with large numbers of individuals by selecting a smaller subset of the population for study. One of the most prevalent types of sampling is random sampling.

Fields of science such as biology, sociology and psychology often study questions about large populations. These could be human populations or animal populations. It is impractical, and usually impossible, to attempt to study or survey every member of a population; studying a sample of that population is a more attainable goal. Simple random sampling is a common approach to sampling and allows all individuals and subsets to have equal weight and probability of being selected. The benefit of simple random sampling is that a truly random sample eliminates all bias. The problems of simple random sampling are randomness and size. It is sometimes difficult to obtain a completely random sample. Also, if the population in question is very large, a small sample, no matter how random, does not account for all of the diversity in the main population.

Systematic sampling is one way to overcome the problems of simple random sampling. Systematic sampling begins with a random sample and then continues with the sampling of every kth element, where k is a population or sample size. A simple example is sampling a long list of people by choosing a random individual from the first 10, and then sampling every 10th person thereafter. Another method, stratified sampling, is useful when a population contains several distinct subsets. In this case, random sampling is conducted within each different subset.