nonprobability sampling

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.

This would be contrasted with nonprobability sampling where arbitrary individuals are selected.

Performing nonprobability sampling can be considerably less expensive than doing probability sampling.

Examples of nonprobability sampling include:

Hence, because the selection of elements is nonrandom, nonprobability sampling does not allow the estimation of sampling errors.

Nonprobability sampling methods include accidental sampling, quota sampling and purposive sampling.

In statistics, self-selection bias arises in any situation in which individuals select themselves into a group, causing a biased sample with nonprobability sampling.

Any use of nonprobability sampling methods (e.g., cut-off or model-based samples) must be justified statistically and be able to measure estimation error.

Conversely, the impossibility of random sampling sometimes necessitates nonprobability sampling, such as convenience sampling or snowball sampling.

'Convenience sampling' (sometimes known as 'grab' or 'opportunity sampling') is a type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand.

Nonprobability sampling is any sampling method where some elements of the population have no chance of selection (these are sometimes referred to as 'out of coverage'/'undercovered'), or where the probability of selection can't be accurately determined.