OVERVIEW

As researchers begin to consider possible tests of their theoretical propositions, they must make a series of important decisions. In this chapter we provide a discussion of choices of population and sample and inferences from samples about populations. We introduce this topic by using examples familiar to political science students – namely, the “plus-or-minus” error figures in presidential horse-race polls, showing where such figures come from and how they illustrate the principles of building bridges between samples we know about with certainty and the underlying population of interest.

POPULATIONS AND SAMPLES

In Chapter 6, we discussed how to use descriptive statistics to summarize large amounts of information about a single variable. In particular, you learned how to characterize a distribution by computing measures of central tendency (like the mean) and measures of dispersion (like the standard deviation). For example, you can implement these formulae to characterize the distribution of income in the United States, or, for that matter, the scores of a midterm examination your professor may have just handed back.

But it is time to draw a critical distinction between two types of data sets that social scientists might use. The first type is data about the population – that is, data for every possible relevant case.