Generalizability relies heavily on the representativeness of the sample (i.e., the extent to which a sample’s composition mirrors that of the population). With probability sampling, members of a population have a known chance of selection, which minimizes selection bias and ensures the sample composition accurately reflects the characteristics of the population. With non-probability sampling, however, SAMPLING BIAS and/or SELF-SELECTION BIAS may result in a non-representative sample: the sample composition does not accurately reflect the characteristics of the population.
Generalizability also relies heavily on sample size. Larger sample sizes “typically do a better job at capturing the characteristics present in the population than do smaller samples” (Meier, Brudney, and Bohte, 2011, p. 178). Larger sample sizes tend to provide more reliable sample statistics and more precise estimates of population parameters. Furthermore, larger sample sizes increase the statistical power of a study, making it easier to identify statistically significant relationships and differences.