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Introduction

At the end of the previous chapter, we explored the Geometric Distribution and the Binomial Distribution. In this chapter, we consider what is called the sampling distribution, which is a term for describing the distribution of a statistic. For example, if we take a survey of a random sample of a population and compute the proportion of “yes” responses to a particular question, then it’s helpful to consider the distribution of all possible sample proportions.

We explore sampling distributions for two common statistics: the sample proportion and the sample mean. We then consider the distributions of a difference of sample proportions and a difference of sample means. In each case, the goal is the same — to describe the center, spread, and shape of the sampling distribution and determine when the normal approximation to the sampling distribution is reasonable.

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