# 2: Sampling Distributions and Confidence Intervals


• 2.1: Sampling Distribution of the Sample Mean
Because our inferences about the population mean rely on the sample mean, we focus on the distribution of the sample mean. Is it normal? What if our population is not normally distributed or we don’t know anything about the distribution of our population? The Central Limit Theorem states that the sampling distribution of the sample means will approach a normal distribution as the sample size increases.
• 2.2: Confidence Intervals
In the preceding chapter we learned that populations are characterized by descriptive measures called parameters. Inferences about parameters are based on sample statistics. We now want to estimate population parameters and assess the reliability of our estimates based on our knowledge of the sampling distributions of these statistics.

This page titled 2: Sampling Distributions and Confidence Intervals is shared under a CC BY-NC-SA 3.0 license and was authored, remixed, and/or curated by Diane Kiernan (OpenSUNY) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.