Probability sampling known chance – UGC NET Question

Probability sampling is characterised by the use of random selection mechanisms that give each element in the population a known, non-zero chance of being included in the sample. This property allows researchers to estimate sampling error and use statistical inference to generalise from the sample to the population. Techniques such as simple random, stratified and systematic sampling fall under this category. Therefore, the sampling procedure described in the stem is correctly called probability sampling.

Option A:
Non-probability sampling does not give every member of the population a known chance of selection, often relying on convenience, judgement or voluntary participation. As a result, sampling error cannot be strictly quantified, and generalisations are more tentative. Hence, non-probability sampling is not the correct completion.

Option B:
Convenience sampling selects whoever is easiest to reach, without any randomisation or known probabilities of selection. It is prone to bias and lacks the formal properties required for probability-based inference, so convenience sampling does not match the stem.

Option C:
In probability sampling, each element’s inclusion likelihood can theoretically be specified, which underpins many statistical tests and confidence interval calculations. This feature aligns exactly with the requirement stated in the question.

Option D:
Judgement sampling, also known as purposive sampling, involves selecting units based on the researcher’s subjective judgement about their relevance or typicality and thus does not provide a known non-zero probability of selection. Therefore, judgement sampling is not appropriate here.