Chapter 9

IN THIS CHAPTER

**Introducing sampling distributions**

**Understanding standard error**

**Approximately simulating the sampling distribution of the mean**

**Attaching confidence limits to estimates**

“Population” and “sample” are pretty easy concepts to understand. A *population* is a huge collection of individuals, and a *sample* is a group of individuals you draw from a population. Measure the sample-members on some trait or attribute, calculate statistics that summarize the sample, and you’re off and running.

In addition to those summary statistics, you can use the statistics to estimate the population parameters. This is a big deal: Just on the basis of a small percentage of individuals from the population, you can draw a picture of the entire population.

How definitive is that picture? In other words, how much confidence can you have in your estimates? To answer this question, you have to have a context for your estimates. How probable are they? How likely is the true value of a parameter to be within a particular lower bound and upper bound?

In this chapter, ...

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