B. Samples and Sampling

Introduction

"Statistical designs always involve compromises between the desirable and the possible."
Leslie Kish. Statistical Designs for Research. 1987. (New York: John Wiley and Sons) p. 1.

As the quote above from Leslie Kish highlights, all research designs involve some form of compromise or adjustment. One of the dimensions on which such compromises are made relates to the populations about which we wish to learn. There are many research questions we would like to answer that involve populations that are too large to consider learning about every member of the population. How have wages of European workers changed over the past ten years? How do Americans feel about the job that the President is doing? What are the management practices of international banking firms?

Questions such as these are important in understanding the world around us, yet it would be impractical, if not impossible, to measure the wages of all European workers, the feelings about the President of all Americans, and the banking practices of the world's banks. Generally, in answering such questions, social scientists examine a fraction of the possible population of interest, drawing statistical inferences from this fraction. The selection process used to draw such a fraction is known as sampling, while the group contained in the fraction is known as the sample.

It is not only statisticians or quantitative researchers that sample. Journalists who select a particular case or particular group of people to highlight in a news story are engaging in a form of sampling. Most of us, in our everyday lives, do some sampling, whether we realize it or not. Although you may not have listened to all the songs of a particular band or singer, you likely would be able to form an opinion about such songs from hearing a few of them. In making such inferences you've relied on a subset of entities (some songs of an artist) to generalize to a larger group (all songs by an artist). You've sampled.