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.

Convenience sampling chooses individuals who are easiest to reach, such as nearby students or colleagues. It does not follow random selection principles and often leads to samples that are not representative of the population. This method is quick and inexpensive but prone to bias. Therefore, selecting respondents because they are easily available is best described as convenience sampling.

Option A:
Systematic sampling selects every kth unit from an ordered list after a random start, which is a probability method. It does not simply pick whoever is easiest to access. So it is not the option described in the stem.

Option B:
Simple random sampling gives each population member an equal and known chance of selection, often using random numbers. It requires a sampling frame and is more rigorous than just asking whoever is nearby. Hence, it is not the correct answer.

Option C:
Stratified sampling divides the population into homogeneous strata and randomly samples from each. It aims at representativeness, not convenience. Therefore, it does not match the description.

Option D:
Option D, convenience sampling, exactly captures the practice of selecting participants because they are readily available, such as sampling friends, colleagues or students in one’s own class. This matches the question, so this option is correct.

Statements A, B, C, D and E accurately describe sampling techniques, whereas F is false. Statement A is true because probability sampling ensures each element has a known non-zero chance of selection. Statement B correctly defines stratified random sampling, and statement C is right in classifying purposive sampling as non-probability. Statement D is correct since snowball sampling is used to access hidden populations, and E is true as probability methods reduce researcher selection bias, while F is false because convenience sampling does not guarantee representativeness.

Option A:
Option A includes all the correct statements and excludes the incorrect one. By presenting A, B, C, D and E together, it reflects standard textbook definitions of sampling techniques and their implications. Since it leaves out F, which wrongly promises guaranteed representativeness, this option is the correct answer.

Option B:
Option B omits statement E, thereby ignoring the important point that probability sampling minimises researcher bias. Although A, B, C and D are true, excluding E makes the combination incomplete. A correct option must list all relevant true statements for the concept.

Option C:
Option C brings F into the combination along with the correct statements. Because F falsely claims that convenience sampling ensures full representativeness, including it contaminates the set of statements. Any option that mixes false and true statements cannot be accepted as correct.

Option D:
Option D also contains F and even omits A, which is central to the definition of probability sampling. Leaving out a key true statement and adding a false one makes this combination doubly problematic. Consequently this option is invalid.

Statement D is wrong because convenience sampling does not guarantee high external validity; it often leads to biased samples that limit generalisation. Statements A, B, C and E are correct: external validity concerns generalisation, narrow or atypical samples restrict it, replication strengthens it and realistic settings can enhance ecological validity. Hence, the only wrong statement is D.

Option A:
Option A is incorrect because it also labels A, which properly defines external validity, as wrong along with D. Treating A as incorrect misrepresents a basic concept in research design.

Option B:
Option B is wrong because it groups B with D as wrong, even though B accurately describes how an atypical sample can limit generalisability. Misclassifying B obscures a key threat to external validity.

Option C:
Option C is also incorrect because it treats C, which rightly notes the value of replication, as wrong. Replication is a standard strategy to test the robustness of findings across contexts.

Option D:
Option D is correct as it singles out D as the only statement that wrongly exaggerates the merits of convenience sampling for generalisation, while implicitly accepting the rest as accurate.

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.

Statements A, B, C and E describe recognised types and uses of non-probability sampling. Purposive sampling focuses on information-rich cases, convenience sampling uses easily available participants, quota sampling fills predetermined subgroup quotas without random selection and snowball sampling helps access hidden populations. Statement D is false because non-probability samples do not support precise calculation of sampling error. Hence the correct combination must include A, B, C and E and exclude D.

Option A:
Option A is incomplete because it omits E, overlooking the important role of snowball sampling for hard-to-reach groups such as marginalised communities or rare populations. Without E, the list of non-probability types is not fully representative.

Option B:
Option B is correct since it includes all true statements and excludes D, which wrongly promises precise sampling error estimates from non-probability designs. It reflects both the strengths and the limitations of such sampling methods.

Option C:
Option C contains B, C and E but omits A, so it ignores purposive sampling, which is a central non-probability technique in qualitative and exploratory research. Its absence makes the option incomplete.

Option D:
Option D includes A, C and E but excludes B, thereby disregarding convenience sampling, which is one of the most common non-probability methods in practice. Therefore this option cannot be the correct one.