UGC NET Questions (Paper – 1)

Simple random sampling selects units from the population such that each has an equal and known probability of inclusion, often using random number tables or computer-generated lists. This method minimises selection bias and allows straightforward estimation of sampling error. When a sampling frame is complete and accurate, simple random sampling is conceptually the most basic probability technique. Thus, the method described in the stem is correctly called simple random sampling.

Option A:
Systematic sampling chooses every kth unit from an ordered list after a random start, which may still approximate equal probabilities but operates through an interval rather than pure individual random draws. It is therefore not identical to simple random sampling.

Option B:
In simple random sampling, combinations of units are also equally likely, which supports many standard statistical procedures based on randomisation theory. This equality of chance matches the stem’s description and makes this option correct.

Option C:
Cluster sampling selects groups rather than individuals as the primary units and may give individuals within larger clusters different probabilities of selection, depending on design.

Option D:
Purposive sampling is non-probability and relies on researcher judgment in selecting cases, so members do not have equal and known chances of inclusion.