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 ...