The Effects of Two Types of Sampling Error on Common Statistical Analyses

Abstract

Sampling error refers to variability that is unique to the sample. If the sample is the entire population, then there is no sampling error. A related point is that sampling error is a function of sample size, as a hypothetical example illustrates. As the sample statistics more and more closely approximate the population parameters, the sampling distributions have less and less variability, and the standard error (standard deviation of the sampling distribution) decreases until there is no sampling error. Sampling error is two dimensional in that the effects of sampling error increase and decrease as a function of how many of the population members are sampled as well as who is sampled. In a one dimensional framework, sampling error is simply the difference between the results one would have obtained if one sampled the entire population and the results one did obtain. An appendix presents a computer program to sample scores randomly. (Contains three figures, two tables, and eight references.) (SLD)