Statistics for People Who (Think They) Hate Statistics

by Neil J. Salkind

as a field, in fact, is all about looking at a sample and inferring to the population it represents. Indeed, the word statistic technically means a number that describes a sample (and the word we use for a number that describes a population is parameter). A measure of how well a sample approximates the characteristics of a population is called sampling error. Sampling error is basically the difference between the values of the sample statistic and the population parameter. The higher the sampling error, the less precision you have in sampling, and the more difficult it will be to make the case that what you find in the sample indeed reflects what you expected to find in the population. And just as there are measures of variability regarding distributions, so are there measures of the variability of this difference between a sample measure and a population measure. This is often called the standard error—it’s basically the standard deviation of the difference between these two values. Samples should be selected from populations in such a way that the sample matches as closely as possible the characteristics of the population. You know, to minimize the sampling error. The goal is to have the sample be as much like the population as possible. The most important implication of ensuring similarity between the two is that the research results based on the sample can be generalized to the population. When the sample accurately represents the population, the results of the study a...