Random Sampling

Randomness

The first critical element in random sampling is the element of randomness. Ideally, all members in the survey's target population have a non-zero chance of selection.

In describing what random sampling is, it is helpful to highlight what it is not. The sample is not pre-determined. Nor is a random sample selected by expert judgment. Random sampling does not imply that the sampling is haphazard. Furthermore, random sampling is not convenience sampling, in which the interviewers take respondents that are easiest to obtain.

The element of randomness is applied to the process scientifically. That is, there is a method, usually mechanical, to the selection process that is rigorously followed. The precise method may rely on random number generators or tables of random numbers. By following the scientific process, the probabilities of selection are known and preserved.

Random number generators and tables of random numbers are not truly random, of course, but the process needs to be random enough. This is especially important in litigious contexts. Bruce D. McCuUough and Wendy Rotz have tested the random number generators available in various data tools and statistical packages.

Known Probabilities Of Selection

The probabilities of selection are important for the theory that enables researchers to estimate sampling error. Because a sample is a subset of the target population and not a census (complete enumeration), estimates derived from sample responses will rarely match the target population values exactly. The variable difference between the sample estimate and the population value is sampling error. (Nonsampling errors, such as inaccurate frames of the target population and imprecise measures of the questionnaire items, affect both surveys and censuses. Nonsampling errors are not covered in this entry.)

Having a randomly selected sample with known probabilities of selection enables the researcher to estimate the sampling error. That is, the researcher can use the sample to make inferences for the target population and to estimate the precision of the sample-based estimates.

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