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Sampling is the use of a subset of the population to represent the whole population. Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. Nonprobability sampling does not meet this criterion and should be used with caution. Nonprobability sampling techniques cannot be used to infer from the sample to the general population.
The advantage of nonprobability sampling is its lower cost compared to probability sampling. However, one can say much less on the basis of a nonprobability sample than on the basis of a probability sample. Of course, research practice appears to belie this claim, because many analysts draw generalizations (e.g., propose new theory, propose policy) from analyses of nonprobability sampled data. One must ask, however, whether those published works are publishable because tradition makes them so, or because there really are justifiable grounds for drawing generalizations from studies based on nonprobability samples.
Some embrace the latter claim, and assert that while probability methods are suitable for large scale studies concerned with representativeness, non-probability approaches are more suitable for in-depth qualitative research in which the focus is often to understand complex social phenomena (e.g., Marshall 1996; Small 2009). These assertions raise an interesting question—how can one understand a complex social phenomenon by drawing only the most convenient expressions of that phenomenon into consideration? What assumption about homogeneity in the world must one make to justify such assertions? Alas, research indicates only one situation in which a non-probability sample can be appropriate—if one is interested only in the specific cases studied (for example, if one is interested in the Battle of Gettysburg), one does not need to draw a probability sample from similar cases (Lucas 2013).
Still, some use non-probability sampling. Examples of nonprobability sampling include:
Even studies intended to be probability studies sometimes end up being non-probability studies due to unintentional or unavoidable characteristics of the sampling method. In public opinion polling by private companies (or other organizations unable to require response), the sample can be self-selected rather than random. This often introduces an important type of error: self-selection bias. This error sometimes makes it unlikely that the sample will accurately represent the broader population. Volunteering for the sample may be determined by characteristics such as submissiveness or availability. The samples in such surveys should be treated as non-probability samples of the population, and the validity of the estimates of parameters based on them unknown.
Statistics, Data, Sampling (statistics), Simple random sample, Quantitative research