Informant accuracy, data validity, and ethical questions—like whether it’s alright to deceive people in conducting experiments—are all measurement problems in research. The other big class of problems involves sampling: Given that your measurements are credible, how much of the world do they represent? How far can you generalize the results of your research?

The answer depends, first of all, on the kind of data in which you’re interested. There are two kinds of data of interest to social scientists: individual attribute data and cultural data. These two kinds require different approaches to sampling.

Individual data are about attributes of individuals in a population. Each person has an age, for example; each person has an income; and each person has preferences for things like characteristics of a mate. If the idea to estimate the average age, or income, or preference in a population—that is, to estimate some population parameters—then a scientifically drawn, unbiased sample is a must.

By “scientifically drawn,’’ I mean random selection of cases so that every unit of analysis has an equal chance of being chosen for study.

Cultural data are different. We expect cultural facts to be shared, so cultural data require experts. If you want to understand a process—like breast-feeding, or the making up of a guest list for a wedding, or a shaman’s treatment of a sick person—then you want people who can offer expert explanations about the cultural norm and about variations on that norm (Handwerker et al. 1997). It’s one thing to ask: ‘‘How many cows did you give to your in-laws as bride price when you got married?’’ It’s quite another thing to ask: ‘‘Why do men who get married around here deliver cows to their in-laws? . . . And how many do men usually give?’’

Individual attribute data require probability sampling; cultural data require nonprobability sampling. This chapter is about the basics of probability sampling, which will take us into a discussion of probability theory, variance, and distributions in chapter 6. We’ll get to nonprobability sampling in chapter 7.