ABSTRACT
Large sets of elementary events are commonly called populations or universes in statistics, but the set theory term sample space is perhaps more descriptive. The term population distribution refers to the distribution of the values of the possible observations in the sample space. Although the characteristics or parameters of the population (e.g., the mean, μ, or the standard deviation, σ) are of both practical and theoretical interest, these values are rarely, if ever, known precisely. Estimates of the values are obtained from corresponding sample values, the statistics. Clearly, for a sample of a given size drawn randomly from a sample space, a distribution of values of a particular summary statistic exists. This simple statement defines a sampling distribution. In statistical practice it is the properties of these distributions that guides our inferences about properties of populations of actual or potential observations. In chapter 6 the binomial, the Poisson, and the normal distributions were discussed. Now that sampling has been examined in some detail, three other distributions and the statistical tests associated with them are reviewed.
