This document provides an overview of sampling and sampling distributions. It begins by stating the chapter goals, which are to describe key sampling concepts like simple random samples and explain the differences between descriptive and inferential statistics. It then defines important terms like population, sample, and sampling distribution. The document explains that sampling is used instead of censuses because it is less time-consuming and costly while still providing sufficiently precise results. It also outlines the chapter, noting it will cover the sampling distributions of the sample mean, sample proportion, and sample variance. It provides examples of how to determine the properties of these sampling distributions such as their means and standard deviations. It emphasizes the central limit theorem and how large samples lead to normally distributed sampling distributions even