This chapter marks a fundamental shift from analyzing individual samples to understanding Sampling Distributions—the distribution of a statistic (like p̂ or x̄) computed from all possible samples of the same size. Students will learn to distinguish between a parameter and a statistic and explore the properties of unbiased estimators.

A major focus is the Central Limit Theorem (CLT), which explains how the sampling distribution of a sample mean becomes approximately Normal as the sample size increases, regardless of the population's shape. Students will also master the conditions for inference, including the 10% condition for independence and the Large Counts/Normal conditions for both proportions and means. Mastering these concepts is essential for the transition to Confidence Intervals and Significance Testing.