UGC NET Questions (Paper – 1)

In this question statements A, B and C appropriately describe the concepts of population, sample and probability sampling. Statement A is true because the population refers to the total set of units about which the researcher wants to generalise. Statement B is correct in defining a sample as a subset of that population, and statement C accurately reflects that in probability sampling every element has a known non-zero chance of selection. Statements D and E are wrong: D incorrectly labels convenience sampling as a rigorous probability technique, and E wrongly suggests that a large sample size alone guarantees representativeness regardless of sampling method, so the wrong statements are exactly D and E.

Option A:
Option A brings together statements D and E, both of which misrepresent sampling concepts. It recognises that convenience sampling is a non-probability method and that large sample size without appropriate sampling design cannot ensure representativeness. Because it neither omits any wrong statements nor includes any correct ones, this combination is the correct answer.

Option B:
Option B treats statement D as the only wrong statement and thus ignores the error contained in statement E. While it is true that calling convenience sampling a rigorous probability method is incorrect, it is equally incorrect to claim that sample size alone guarantees representativeness. Since this option fails to capture all the wrong statements, it cannot be accepted.

Option C:
Option C identifies only statement E as wrong and overlooks statement D. It is right to challenge E because representativeness depends on the sampling technique and frame, not just on the size. However, by failing to mark D as wrong, this option does not fully answer the question.

Option D:
Option D erroneously includes statement C among the wrong statements. Statement C correctly defines probability sampling as giving every element a known non-zero chance of selection. By classifying a correct statement as incorrect, this combination becomes logically inconsistent and therefore cannot be the correct answer.

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.

Probability sampling methods ensure that every element in the population has a known, non zero chance of being selected. Simple random sampling is a fundamental probability method where each member has an equal chance of inclusion, often achieved by lottery or random number procedures. This enhances representativeness and allows the use of statistical inference.

Option A:
Option A, convenience sampling, is a nonprobability method where respondents are chosen purely on ease of access. There is no known probability of selection for each member of the population, so it does not qualify as probability sampling.

Option B:
Option B, purposive sampling, involves selecting cases based on researcher judgment and is also nonprobability in nature. It is suitable for specialized studies but does not provide equal chance to all population elements.

Option C:
Option C clearly matches the definition of probability sampling because it selects from a complete list using random procedures. This ensures each unit has a known probability of selection and supports generalization within calculated error limits.

Option D:
Option D, quota sampling, fixes proportions for certain categories but usually relies on convenience within those categories. Because selection within quotas is not random, it is considered nonprobability sampling.

Purposive sampling involves deliberately selecting participants or cases that are expected to yield the most relevant, detailed or insightful information for the research question. The researcher uses judgement based on theory, experience or expert knowledge to choose these cases. This method is common in qualitative studies where depth of understanding is more important than statistical representativeness. Hence, selecting information-rich cases for in-depth study is accurately termed purposive sampling.

Option A:
In purposive sampling, the researcher might choose key informants, typical cases or extreme cases depending on the study’s objectives. The emphasis is on relevance and richness of data rather than on probability-based selection. These characteristics correspond closely to the description in the stem.

Option B:
Random sampling gives each element of the population a known, usually equal, chance of selection and is used primarily in quantitative research for statistical generalisation. It does not rely on the researcher’s judgement about which cases are information-rich. Therefore, random sampling is not the correct completion.

Option C:
Quota sampling involves filling predetermined numbers of participants in specified categories, such as age or gender, often using convenience procedures. Although it may achieve some diversity, it is not focused primarily on depth or richness of information, so quota sampling does not match the stem.

Option D:
Systematic sampling selects every kth unit from an ordered list after a random start, and is a probability sampling method. It is driven by a mechanical rule rather than by the researcher’s judgement about information richness, making it inappropriate as an answer here.

Snowball sampling begins with a small number of initial participants who meet the inclusion criteria, and these participants then refer others they know who also fit the criteria. The sample grows like a snowball rolling downhill, making this approach particularly useful for hard-to-reach or hidden populations. Because the selection process depends on networks rather than known probabilities, it is a non-probability technique. Thus, the chain referral procedure described in the stem is accurately called snowball sampling.

Option A:
Quota sampling involves filling predetermined numbers of units in specified categories, such as age or gender, using convenient procedures. It does not rely on participants recruiting other participants through personal networks. Therefore, quota sampling does not match the recruitment process outlined in the question.

Option B:
Stratified sampling is a probability method where the population is divided into homogeneous strata and random samples are drawn from each stratum. It is based on randomisation rather than on referrals from existing participants. Hence, stratified sampling is not the correct answer.

Option C:
Systematic sampling selects every kth element from an ordered list after a random start, which again does not involve social networks or referrals. It is a probability technique quite different from the non-probability chain approach described in the stem. Thus, systematic sampling is not appropriate here.

Option D:
Snowball sampling is especially valuable when no complete sampling frame is available and when trust within networks is needed for access. These characteristics align exactly with the description of participants recruiting acquaintances, confirming it as the right term.

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.

Quota sampling requires the researcher to identify relevant categories, such as gender or age groups, and decide how many units from each category should be included. Participants are then selected conveniently until each quota is filled, without knowing the exact probability of selection. This makes it a non-probability technique that still attempts to mirror some characteristics of the population. Because the stem mentions filling a pre-specified number of units in categories, quota sampling is the correct term.

Option A:
Snowball sampling relies on existing participants to recruit further participants from among their acquaintances, especially in hard-to-reach populations. It does not involve setting numerical quotas for specific categories in advance, so it does not fit the description given.

Option B:
Cluster sampling is a probability method that selects intact groups like schools or villages, usually by random procedures, and is not primarily defined by filling quotas in categories. Therefore, cluster sampling is not the appropriate completion for the stem.

Option C:
Purposive sampling selects participants based on the researcher’s judgment about which cases are most informative, without necessarily specifying fixed numbers in each category. While it is also non-probability, it emphasises information-rich cases rather than quota fulfilment. Hence, purposive sampling is not the best answer.

Option D:
Quota sampling is often used in opinion polls and market research when quick, inexpensive samples are needed, though it may introduce bias due to non-random selection. Its defining feature is matching predefined category counts, which aligns directly with the stem’s description.

Snowball sampling is a non-probability method used especially when the population is hard to identify or reach, such as hidden or stigmatised groups. In this technique, the researcher first accesses a few participants and then asks them to refer others they know who fit the criteria. The sample thus grows like a snowball rolling and accumulating more snow. Because the stem describes existing participants recruiting future participants from their acquaintances, it refers to snowball sampling.

Option A:
Quota sampling involves setting quotas for specific subgroups in the population and then selecting participants, often conveniently, until each quota is filled. It does not rely on participants recruiting one another. Therefore, quota sampling does not match the chain-referral process described in the stem.

Option B:
Convenience sampling selects participants who are easily available to the researcher, such as students in a particular class or people in a nearby location. It does not depend on referrals from current participants and lacks the network-based growth characteristic of snowball sampling. Hence, convenience is not the correct answer.

Option C:
Purposive sampling is based on the researcher’s judgment about which cases are most informative, not primarily on referrals from existing participants. Although snowball sampling may start with purposively chosen initial participants, its defining feature is the subsequent chain referral. Thus, purposive sampling is not what the stem is describing.

Option D:
Snowball sampling is particularly useful for accessing populations that lack complete sampling frames, as participants’ social networks become pathways into otherwise inaccessible communities. This network-based, participant-recruited expansion is exactly what the question outlines, confirming snowball as the appropriate completion.