In This Chapter

  • The reason for measuring a sample rather than the population
  • The various methods for collecting a random sample
  • Defining sampling errors and sampling bias
  • Consequences for poor sampling techniques

This first chapter dealing with the long-awaited topic of inferential statistics focuses on the subject of sampling. Way back in Chapter 1, we defined a population as representing all possible outcomes or measurements of interest, and a sample as a subset of a population. Here we’ll talk about why we use samples in statistics and what can go wrong if they are not used properly.

Virtually all statistical results ...

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