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Statistical sampling plays a vital role in understanding and making inferences with respect to all types of populations and is especially salient in a world where populations are large and big data is the new standard. In an ideal situation, samplers have access to a list of all the units in a population, called a sampling frame, from which to draw a sample. This allows them to select individual population units with known probabilities, producing a probability sample (Lohr, 2010). A sample for which probabilities of selection are not known is called a non-probability sample. Probability samples are preferred to non-probability samples because the sampling variance of the estimators calculated from probability samples can be determined using standard sampling theory. The primary disadvantage of non-probability samples is the potential for biased estimation stemming from undercoverage and a lack of representativeness in the samples. Without external sources of information, these sample deficiencies cannot be detected.

Combining a Probability and a Non-Probability Sample in a Capture-Recapture Setting