Nonprobability

Nonprobability, in statistics and research methods, refers broadly to sampling or inference procedures in which the probability of including each unit in the sample is unknown, uneven, or not based on random selection. This contrasts with probability sampling, where every member of a population has a known nonzero chance of selection. Nonprobability methods are commonly used in social sciences, market research, and exploratory studies when sampling frames are incomplete, populations are hidden, or quick results are needed.

Common nonprobability sampling techniques include convenience sampling (selecting readily available subjects), judgment or purposive sampling (choosing units perceived to be typical or knowledgeable), quota sampling (ensuring the sample mirrors certain characteristics), and snowball sampling (existing subjects recruit others). These methods can incorporate theoretical or practical knowledge to target specific groups or phenomena.

Advantages include lower cost, faster data collection, and practicality in hard-to-reach groups. Limitations include potential bias, lack of representativeness, and the inability to calculate sampling error or generalize findings to a population with known confidence levels. Researchers using nonprobability methods typically acknowledge limitations and may triangulate with other data sources or use probabilistic methods in later stages to strengthen inferences.

In reporting, nonprobability sampling is described with caveats about generalizability, while results are presented as insights into the sampled individuals or settings rather than population-wide estimates.