In This Chapter
◆ The reason for measuring a sample rather than the population
◆ The various methods for collecting a random sample
◆ Defining sampling errors
◆ 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 are based on the measurements of a sample drawn from a population. Major decisions are often ...