UGC NET Questions (Paper – 1)

Statements A, B, D and E correctly describe why and how sampling is used in research. Sampling helps when studying an entire population is impractical, and a well-drawn sample can yield useful estimates about population parameters. Probability sampling ensures known selection chances, and practical issues such as time and access must be considered in sampling decisions. Statement C is false because convenience samples may be biased and are not guaranteed to be representative.

Option A:
Option A is incomplete because it omits D and E, both of which highlight key aspects of sampling such as probability-based selection and practical constraints. Without these, the combination underplays how representativeness is achieved and what factors shape sampling choices.

Option B:
Option B is correct because it combines A, B, D and E, the set of statements that align with standard sampling principles. It rightly excludes C, which overstates the merits of convenience sampling. The option therefore captures both methodological and practical dimensions of sampling.

Option C:
Option C is incorrect because it includes C, which wrongly claims that convenience sampling always produces representative samples. Convenience samples are often prone to bias, so including C corrupts the otherwise correct set of statements.

Option D:
Option D is wrong because it omits A, failing to mention the basic rationale for using samples rather than censuses. It also does not explicitly recognise that sampling is a response to practical limitations, which is central to its role in research.

Nonprobability sampling is often used when it is difficult or impossible to identify every element of the population or when the focus is on information rich cases rather than representativeness. For specialized topics, the researcher may deliberately select experts or key informants with deep knowledge.

Option A:
This option suggests that nonprobability sampling is used when a full frame and easy random selection are available, which is the opposite of recommended practice; in such circumstances, probability sampling is usually preferred.

Option B:
This option is correct because it points to situations with ill-defined populations and the need for expertise. Selecting knowledgeable individuals purposively is then more practical than attempting random sampling.

Option C:
Option C describes a goal of estimating population parameters with known sampling error, which is best served by probability sampling methods, not nonprobability ones.

Option D:
Option D refers to legal requirements for random procedures—again a context where probability sampling, not nonprobability sampling, is appropriate.