The term population refers to a collection of people or objects that share common observable characteristics.

**WHAT ARE POPULATIONS AND SAMPLES?**

The term population refers to a collection of
people or objects that share common observable characteristics. For example, a
population could be all of the people who live in your city, all of the
students enrolled in a particular university, or all of the people who are
afflicted by a certain disease (e.g., all women diagnosed with breast cancer
during the last five years). Generally, researchers are interested in
particular characteristics of a population, not the characteristics that define
the population but rather such attributes as height, weight, gender, age, heart
rate, and systolic or diastolic blood pressure.

Recall the approaches of statistics (descriptive
and inferential) discussed in Chapter 1. In making inferences about populations
we use samples. A sample is a subset of the population.

In this chapter we will discuss techniques for
selecting samples from populations. You will see that various forms of random
sampling are preferable to nonrandom sampling because random sample designs
allow us to apply statistical methods to make inferences about population
characteristics based on data collected from samples.

When describing the attributes of populations,
statisticians use the term parameter. In this text, the symbol *μ* will be used to denote a
population parameter for the average (also called the mean or expected value).
The corresponding estimate from a sample is called a statistic. For the sample
estimate, the mean is denoted by .

Thus, it is possible to refer to the average height
or age of a population (the parameter) as well as the average height of a
sample (a statistic). In fact, we need inferential statistics because we are
unable to determine the values of the population parameters and must use the
sample statistics in their place. Using the sample statistic in place of the
population parameter is called estimation.

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