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.

Statements A, B, C and E correctly describe common sampling methods, whereas D is false. Simple random sampling gives all units equal selection probability, cluster sampling selects intact groups, systematic sampling chooses every k-th unit after a random start and snowball sampling is indeed useful for reaching hidden populations through referrals. Purposive sampling, however, is a non-probability method because units are intentionally chosen based on specific characteristics rather than random procedures.

Option A:
Option A omits statement E, thereby ignoring snowball sampling as a valid non-probability technique for hard-to-reach groups. While A, B and C are correct, the absence of E makes the combination incomplete.

Option B:
Option B is correct because it includes all four accurate descriptions and excludes D, which misclassifies purposive sampling as probability-based. It reflects the standard classification of sampling methods in research methodology.

Option C:
Option C wrongly includes D, which incorrectly labels purposive sampling as a probability technique, and omits B, which is a correct description of cluster sampling. This mixture of omission and error makes the option flawed.

Option D:
Option D includes B, C and E but leaves out A, neglecting the fundamental simple random sampling method. Because it fails to mention all true statements from the list, it cannot be the correct answer.

Option E contains D, the incorrect statement about purposive sampling, and omits A, the definition of simple random sampling. As a result, it provides an inaccurate view of sampling methods.

Statements A and B correctly describe simple random and stratified sampling. Statement C is also true as cluster sampling initially chooses whole clusters for inclusion. Statement E is correct because systematic sampling indeed selects every nth unit after a random start. Statement D is false since convenience sampling is non-probability based and can easily introduce bias. Thus, A, B, C and E only are correct.

Option A:
Option A is incomplete as it omits E (systematic sampling).

Option B:
Option B is wrong because it includes D (false).

Option C:
Option C is correct because it unites all of the true descriptions and excludes D.

Option D:
Option D is incorrect since it retains D (false) and omits A.