In this chapter, we define the terms population and sample and present several methods for selecting samples.

**Defining Populations and Selecting Samples**

Chapter 1 provided an introduction to the field of
biostatistics. We discussed applications of statistics, study designs, as well
as descriptive statistics, or exploratory data analysis, and inferential
statistics, or confirmatory data analysis. Now we will consider in more detail
an aspect of inferential statisticsâ€”sample selectionâ€”that relates directly to
our ability to make inferences about a population.

In this chapter, we define the terms population and
sample and present several methods for selecting samples. We present a
rationale for selecting samples and give examples of several types of samples:
simple random, convenience, systematic, stratified random, and cluster. In
addition, we discuss bootstrap sampling because of its similarity to simple
random sampling. Bootstrap sampling is a procedure for generating bootstrap
estimates of parameters, as we will demonstrate in later chapters. Detailed
instructions for selecting simple random and bootstrap samples will be
provided. The chapter concludes with a discussion of an important property of
random sampling, namely, unbiasedness.

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