UGC NET Questions (Paper – 1)

Probability sampling is defined by the use of random selection mechanisms that give each member of the population a known, non-zero chance of inclusion. This property allows researchers to estimate sampling error and to generalise findings from the sample to the population with quantifiable confidence. Techniques such as simple random, stratified and cluster sampling fall under this category. Because the stem explicitly mentions a known non-zero chance of selection for every element, it is describing probability sampling.

Option A:
Quota sampling is a non-probability method where the researcher fills pre-specified quotas for certain subgroups, often using convenience within each quota. Individual elements do not have known chances of selection, and probability theory cannot be applied to estimate sampling error. Thus, quota sampling does not satisfy the conditions stated in the stem.

Option B:
Purposive sampling involves selecting cases that are judged to be information-rich or particularly relevant to the research question, based on the researcher’s judgement. It does not assign known probabilities of selection to all population members, so it is not probability sampling. Therefore, purposive sampling cannot be the correct option.

Option C:
Convenience sampling selects units that are easiest to access, such as volunteers or people who are readily available. This method ignores the requirement that every member of the population have a known non-zero chance of selection, and it often yields biased samples. Consequently, convenience sampling does not meet the criteria in the question.

Option D:
Probability sampling uses objective randomisation procedures, such as random number tables or computer-generated random lists, to ensure fairness in selection. Because each element’s chance of being chosen is known, researchers can make statistically grounded inferences to the population. This matches exactly what the stem describes, making probability sampling the correct answer.

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.