Abstract
This chapter provides some of the tools that are used in probability theory as measures of uncertainty, and particularly those tools that allow making inferences and evaluating the reliability of such inferences. It outlines the concept of a sampling distribution, which is a probability distribution that describes the way a statistic from a random sample is related to the characteristics of the population from which the sample is drawn. It presents the concept of the probability of a simple outcome of an experiment such as the probability of obtaining a head on a toss of a coin. Rules are then given for obtaining the probability of an event, which may consist of several outcomes such as obtaining no heads in the toss of five coins. These rules are used to construct probability distributions that are simply listings of probabilities of all events resulting from an experiment such as obtaining all possible number of heads in the toss of five coins. This concept is generalized to define probability distributions for the results of experiments that result in continuous numeric variables. Some of these distributions are derived from purely mathematical concepts and require the use of functions and tables to find probabilities.
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