Introduction

Distribution theory is concerned with probability distributions of random variables, with the emphasis on the types of random variables frequently used in the theory and application of statistical methods. For instance, in a statistical estimation problem we may need to determine the probability distribution of a proposed estimator or to calculate probabilities in order to construct a confidence interval.

Clearly, there is a close relationship between distribution theory and probability theory; in some sense, distribution theory consists of those aspects of probability theory that are often used in the development of statistical theory and methodology. In particular, the problem of deriving properties of probability distributions of statistics, such as the sample mean or sample standard deviation, based on assumptions on the distributions of the underlying random variables, receives much emphasis in distribution theory.

In this chapter, we consider the basic properties of probability distributions. Although these concepts most likely are familiar to anyone who has studied elementary probability theory, they play such a central role in the subsequent chapters that they are presented here for completeness.

Basic Framework

The starting point for probability theory and, hence, distribution theory is the concept of an experiment. The term experiment may actually refer to a physical experiment in the usual sense, but more generally we will refer to something as an experiment when it has the following properties: there is a well-defined set of possible outcomes of the experiment, each time the experiment is performed exactly one of the possible outcomes occurs, and the outcome that occurs is governed by some chance mechanism.