Chapter 6
Sampling Distributions and the Central Limit Theorem
IN THIS CHAPTER
Understanding the concept of a sampling distribution
Using the Central Limit Theorem
Determining the factors that affect precision
When you take a sample of data, it’s important to realize the results will vary from sample to sample. Statistical results based on samples should include a measure of how much they expect those results to vary from sample to sample. This chapter shows you how to do that by couching everything in terms of the sample means (for numerical data) and applying the same ideas to sample proportions (for categorical data).
Sampling Distributions
Suppose everyone on the planet rolled a single die and recorded the outcome, X. With all those outcomes, we’d have an entire population of values. The graph of these outcomes in the population would represent the distribution of X. Now suppose everyone rolled his or her die 10 times (a sample of size 10) and recorded the average,
. With all those averages, we’d get an entirely new population — the population of sample means. The graph of ...