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.

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.

Quota sampling requires the researcher to identify relevant categories, such as male and female or different age groups, and decide how many respondents are needed in each. Field workers then select convenient individuals until the quotas for each category are filled. While this ensures some diversity, it does not use random selection, so it remains a non-probability method. Therefore, the technique described in the stem is known as quota sampling.

Option A:
Cluster sampling is a probability method involving selection of intact groups such as schools or villages. It does not focus on filling pre-set category counts with conveniently available individuals. Thus, it is not the correct answer.

Option B:
Purposive sampling selects cases judged to be especially informative or typical based on the researcher’s judgment. It does not rely on fixed numerical quotas for categories, so it does not match the description.

Option C:
Option C, quota sampling, is widely used in market research and opinion polls when a full sampling frame is unavailable. It imitates some features of stratification but without randomisation. This matches the idea of filling predetermined numbers in categories, making it correct.

Option D:
Systematic sampling is a probability method selecting every kth unit from a list. It does not aim to satisfy numerical quotas in predefined categories using convenience selection. Therefore, it is not appropriate here.

Snowball sampling starts with a small number of initial participants who meet the inclusion criteria and then asks them to refer others they know who also fit the criteria. This process continues, expanding the sample like a rolling snowball. It is especially useful for accessing hard-to-reach or hidden populations where no sampling frame exists. Thus, the referral-based method described in the stem is correctly called snowball sampling.

Option A:
Quota sampling sets numerical targets for categories like age or gender and then uses convenience methods to fill those quotas, without relying on participant referrals. It does not match the chain-referral strategy highlighted in the stem.

Option B:
Snowball sampling leverages social networks to identify additional participants, which can be efficient but may introduce bias because individuals tend to refer others similar to themselves. This characteristic aligns exactly with the description of recruiting subjects from acquaintances, so this option is correct.

Option C:
Stratified sampling is a probability method where the population is divided into strata and random samples are drawn from each, which requires a sampling frame rather than referrals.

Option D:
Systematic sampling selects every kth unit from an ordered list after a random start and does not depend on participants bringing in new members.

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.

Statements A, B, C and E correctly describe typical qualitative sampling practices. Purposive sampling is widely used to select cases that yield rich information, and the goal is depth rather than statistical representativeness. Theoretical sampling helps refine emerging categories, and sample size is often guided by saturation, when new data no longer add substantially new insights. Statement D is false because probability sampling is not mandatory in qualitative research and is often not used.

Option A:
Option A is correct because it brings together the four true statements and excludes D, which misrepresents qualitative sampling requirements. It portrays qualitative sampling as flexible, concept-driven and oriented toward information richness rather than probability.

Option B:
Option B is incomplete because it omits E, ignoring how the concept of saturation influences decisions about when to stop collecting data. Without E, the dynamic nature of sample size determination in qualitative work is underplayed.

Option C:
Option C is incorrect because it includes D, the statement that probability sampling is mandatory, which contradicts common qualitative practice. Including D alongside true statements creates a misleading picture of qualitative methodology.

Option D:
Option D is wrong because it retains D and drops B, failing to acknowledge that depth of understanding, rather than representativeness, is the usual goal. This combination therefore distorts both the purpose and the technique of qualitative 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.

Non-probability sampling refers to techniques in which elements of the population are selected using non-random procedures such as convenience, purposive or quota sampling. In these methods, the exact probability of any given element being included in the sample is unknown. This limits the ability to generalise findings using statistical inference. Because the stem explicitly states that selection probabilities are unknown, non-probability sampling is the correct answer.

Option A:
Probability sampling, by contrast, ensures that each element of the population has a known, non-zero chance of selection, often through random processes. This property enables estimation of sampling error and supports strong generalisation to the population. Since the question mentions unknown probabilities, probability sampling cannot complete the stem correctly.

Option B:
Non-probability sampling is often used in exploratory or qualitative studies, or when constructing sampling frames is difficult or impossible. Although it may introduce bias, it can be practical and informative in certain contexts. These characteristics fit the description provided in the stem, confirming non-probability as the appropriate term.

Option C:
Systematic sampling, a form of probability sampling, selects every kth element from an ordered list after a random start, so individual selection probabilities are known. Thus, systematic sampling does not align with the condition of unknown probabilities.

Option D:
Stratified sampling, another probability method, divides the population into homogeneous groups and draws random samples from each stratum. Here again the chance of selection is known and can be calculated, which contradicts the stem. Therefore, stratified sampling is not the right completion.

Purposive sampling, also called judgmental sampling, involves selecting units based on the researcher’s judgment about which cases will be most informative or relevant to the research question. It is commonly used in qualitative studies where depth of understanding is more important than statistical representativeness. The researcher may choose typical cases, extreme cases or key informants. Because the stem describes selecting information-rich cases for in-depth study, it clearly refers to purposive sampling.

Option A:
Simple random sampling gives every element of the population an equal chance of selection using random methods such as tables or generators. It is not based on the researcher’s judgment about which cases are most informative. Therefore, simple random does not fit the description in the stem.

Option B:
Convenience sampling selects units that are easiest to access, such as friends, students in a class or people in a nearby location, regardless of their information richness. Although convenient, it can lead to biased samples and is conceptually different from purposive selection of particularly informative cases. Hence, convenience is not the correct answer.

Option C:
Purposive sampling is guided by theoretical and practical considerations concerning which cases can shed the most light on the phenomenon, and it is often combined with qualitative methods like interviews or observations. This targeted selection aligns exactly with the idea of information-rich cases highlighted in the stem, making purposive the proper completion.

Option D:
Snowball sampling involves asking initial participants to refer others in their networks, which is useful for hard-to-reach populations. While it can also lead to information-rich participants, the defining feature is chain referral, not direct judgmental selection by the researcher. Thus, snowball sampling is not the best label for the situation described.

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.

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.