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5
Sampling and Confidence Intervals
LEARNING OBJECTIVES
Define a “good” sample and how to obtain one.
List three facts derived from the central limit theorem.
Calculate the range within which a population mean probably falls.
CHAPTER OVERVIEW
This chapter starts by expanding on concepts introduced in previous chapters and revisiting the differences between populations and samples. The discussion then turns to the different ways that samples are gathered and to the criteria for a “good” sample. Next, sampling distributions and the central limit theorem are introduced, two concepts that are part of the foundation for statistical decision making, which is introduced in the next chapter. To cap off this chapter and to presage the logic of hypothesis testing, a practical application of the central limit theorem, the 95% confidence interval for the population mean is introduced. This confidence interval uses the sample mean to calculate a range, an interval, that we are reasonably certain captures the population mean.
5.1 Sampling and Sampling Error
5.2 Sampling Distributions and the Central Limit Theorem
5.3 The 95% Confidence Interval for a Population Mean