Andrew F. Hayes

*Ohio State University*

Researchers are usually interested in making some kind of an inference from the data obtained from the sample – a ‘generalization’ of some sort. However, practical considerations typically require the researcher to limit his or her data collection to a sample drawn from a larger population of interest. The ability to make a population inference is going to depend in large part on how the sample was obtained, for the method chosen influences how similar the sample is to the population on all dimensions, characteristics, or features that are likely to influence or be related to the measurement of the variables in the study. When population inference is the goal the researcher is well advised to employ some kind of random sampling method.

Random sampling (also called ‘probability’ or ‘probabilistic’ sampling) requires that the process through which members of the population end up in the sample be determined by chance. Furthermore, for each member of the population, it must be possible to derive the probability of inclusion in the sample (even if you never actually calculate that probability). Random sampling is extremely important when the goal of the research is population inference, for it is the random sampling process that will, over the long haul, produce a sample that represents the population. Although it is possible that, just by chance, a specific sample will be unrepresentative of the population as a whole on one ...

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