UGC NET Questions (Paper – 1)

Cluster sampling involves dividing the population into natural groups, or clusters, such as schools, villages or classes, and then randomly selecting some of these clusters for inclusion in the sample. All or many members within chosen clusters are then studied. This method is often used when a complete list of individuals is difficult to obtain but a list of groups is available. Since the stem describes selecting intact groups like schools as sampling units, cluster sampling is the correct term.

Option A:
Simple random sampling selects individual units from the entire population so that each has an equal chance of inclusion, without grouping them into clusters. It does not specifically use intact groups as the units of selection, so it does not match the situation in the stem.

Option B:
Stratified random sampling first divides the population into homogeneous strata based on characteristics such as gender or region and then draws random samples from each stratum. The primary aim is proportional representation of strata, not sampling intact groups as units. Therefore, stratified random sampling is not the best answer here.

Option C:
Cluster sampling is particularly useful when the population is geographically dispersed, because studying selected clusters reduces travel and administrative costs. However, it may increase sampling error compared to simple random sampling. The defining feature of using intact groups matches the description in the question.

Option D:
Quota sampling is a non-probability technique in which researchers fill predetermined quotas for subgroups using convenience procedures, not random selection of clusters. It does not require intact groups such as schools to be the sampling units, so quota is not appropriate.

Statement D is wrong because probability sampling typically requires a sampling frame or some representation of the population to ensure known selection probabilities. Statements A, B, C and E are correct: the sampling frame is the list from which samples are drawn, its quality affects representativeness, outdated frames cause coverage errors and an ideal frame covers the entire target population. Therefore, the only wrong statement is D.

Option A:
Option A is incorrect because it also labels A, which correctly defines the sampling frame, as wrong along with D. This misrepresents a basic concept in sampling.

Option B:
Option B is wrong because it groups C with D as wrong, even though C accurately states that outdated frames cause coverage errors. Coverage error is a standard threat to representativeness.

Option C:
Option C is correct because it isolates D as the sole statement inconsistent with probability sampling principles and implicitly accepts A, B, C and E as true.

Option D:
Option D is incorrect because it treats B and E as wrong along with D, despite both B and E properly describing the relationship between frame quality and sample representativeness.

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.

Stratified random sampling involves dividing the population into relatively homogeneous strata based on characteristics such as gender, region or discipline and then drawing random samples from each stratum. This ensures that important subgroups are adequately represented in the final sample. It can increase the precision of estimates, especially when subgroup differences are substantial. Because the stem mentions dividing the population into homogeneous subgroups and sampling from each, it describes stratified random sampling.

Option A:
Stratified random sampling uses random selection within each stratum, preserving probabilistic principles while controlling subgroup representation. This approach leads to a sample that mirrors the composition of the population with respect to chosen stratification variables. These features align exactly with the process outlined in the stem, making stratified random the correct answer.

Option B:
Simple random sampling selects units from the population without any prior subdivision, giving each element an equal chance of being chosen, but it does not guarantee representation of specific subgroups. Since the question specifies dividing the population into subgroups first, simple random is not the best match.

Option C:
Systematic sampling selects every kth element from an ordered list after a random start, without necessarily dividing the population into strata. Although it is a probability method, it does not follow the subgroup-based approach described in the stem. Therefore, systematic is not appropriate here.

Option D:
Cluster sampling divides the population into clusters, often based on natural groupings such as schools or villages, and then selects some clusters, usually sampling all or many elements within chosen clusters. Clusters are not necessarily homogeneous, and the method focuses on groups rather than ensuring representation from all subgroups. Thus, cluster sampling does not fit the description given.

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.

Systematic sampling involves ordering the population list, choosing a random starting point within the first k elements and then selecting every kth unit thereafter. This procedure is easier to implement than simple random sampling while still approximating randomness if the list has no hidden periodicity. The constant interval of selection is its hallmark. Hence, the kth selection method described in the stem is known as systematic sampling.

Option A:
In systematic sampling, the sampling interval k is determined by dividing the population size by the desired sample size. Provided that the ordering of units is not correlated with the variable of interest, this method yields a reasonably representative probability sample. These properties align closely with the description given in the question.

Option B:
Simple random sampling gives each unit an equal and independent chance of selection, usually by random numbers, but it does not use a fixed interval to pick every kth unit. Therefore, simple random sampling is not the precise term for the process mentioned.

Option C:
Cluster sampling selects intact groups such as schools or villages, not individual units at fixed intervals from a list. Its logic and implementation differ significantly from the systematic interval approach. Thus, cluster sampling is not correct here.

Option D:
Purposive sampling is a non-probability technique where the researcher deliberately selects cases judged to be most informative, without randomisation or a fixed interval rule. It does not match the ordered list and kth selection method described in the stem.

Probability sampling is defined by the use of random selection mechanisms that give each member of the population a known, non-zero chance of inclusion. This property allows researchers to estimate sampling error and to generalise findings from the sample to the population with quantifiable confidence. Techniques such as simple random, stratified and cluster sampling fall under this category. Because the stem explicitly mentions a known non-zero chance of selection for every element, it is describing probability sampling.

Option A:
Quota sampling is a non-probability method where the researcher fills pre-specified quotas for certain subgroups, often using convenience within each quota. Individual elements do not have known chances of selection, and probability theory cannot be applied to estimate sampling error. Thus, quota sampling does not satisfy the conditions stated in the stem.

Option B:
Purposive sampling involves selecting cases that are judged to be information-rich or particularly relevant to the research question, based on the researcher’s judgement. It does not assign known probabilities of selection to all population members, so it is not probability sampling. Therefore, purposive sampling cannot be the correct option.

Option C:
Convenience sampling selects units that are easiest to access, such as volunteers or people who are readily available. This method ignores the requirement that every member of the population have a known non-zero chance of selection, and it often yields biased samples. Consequently, convenience sampling does not meet the criteria in the question.

Option D:
Probability sampling uses objective randomisation procedures, such as random number tables or computer-generated random lists, to ensure fairness in selection. Because each element’s chance of being chosen is known, researchers can make statistically grounded inferences to the population. This matches exactly what the stem describes, making probability sampling the correct answer.

Stratified random sampling involves partitioning the population into strata that are internally homogeneous on some relevant characteristic, such as gender or region, and then drawing random samples from each stratum. This ensures that each subgroup is adequately represented in the overall sample. It can improve precision of estimates, especially when there are substantial differences between strata. Therefore, the method described in the stem is accurately called stratified random sampling.

Option A:
Cluster sampling selects intact groups or clusters, such as schools or villages, and then includes all or many members of selected clusters. It does not require that clusters be homogeneous internally on a characteristic used for stratification, so it differs from the technique in the stem.

Option B:
Systematic sampling chooses every kth element from an ordered list after a random start and does not involve dividing the population into homogeneous subgroups first. Thus, systematic sampling is not the correct completion.

Option C:
Convenience sampling selects cases that are easiest to access without randomisation or prior formation of strata, typically producing a non-probability sample. It does not guarantee representation of all subgroups, so convenience sampling is not suitable here.

Option D:
Stratified random sampling can use proportional or disproportionate allocation depending on research needs, but in all cases it explicitly draws random samples within each stratum. This defining feature corresponds perfectly to the description in the question.

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.

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.

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.

Cluster sampling selects naturally occurring groups, or clusters, such as classrooms, villages or city blocks, rather than individual elements at the first stage. After clusters are randomly chosen, all or a sample of elements within each cluster are studied. This method is particularly useful when a complete list of individuals is unavailable but a list of clusters exists. Thus, the technique described in the stem is correctly called cluster sampling.

Option A:
Systematic sampling chooses every kth element from an ordered list after a random start and does not focus on intact groups such as villages. Its selection pattern is interval-based, not cluster-based. So it is not the right option.

Option B:
Stratified sampling divides the population into homogeneous strata and draws random samples from each stratum. It ensures representation of each subgroup but does not necessarily treat intact groups as primary units. Therefore, it does not match the stem.

Option C:
Quota sampling is a non-probability method in which the researcher fills predetermined numbers in specific categories using convenience selection. It is not a cluster-based probability technique. Hence, it cannot complete the blank.

Option D:
Option D, cluster, explicitly involves random selection of group units followed by study of members within those units. This is exactly the approach described in the question, making this option correct.

Sampling error is the natural discrepancy between a statistic computed from a sample and the true but unknown population parameter. It occurs because a sample, no matter how well drawn, captures only part of the population’s variability. As sample size increases and sampling procedures are sound, sampling error tends to decrease. Thus, the difference described in the stem is correctly termed sampling error.

Option A:
Measurement error arises from inaccuracies in instruments, data recording or respondent misunderstanding and is not solely due to using a subset of the population. It reflects flaws in measurement rather than in sampling.

Option B:
Non-response error occurs when certain selected individuals do not participate, potentially biasing results if non-respondents differ systematically from respondents. While related to sampling, it is a specific type of non-sampling error.

Option C:
Response bias refers to systematic tendencies for respondents to answer inaccurately, such as social desirability bias, and is independent of whether a sample or census is used. It is not the error defined here.

Option D:
Sampling error arises even under proper random sampling and is expected whenever one uses a sample instead of a census. It is the discrepancy highlighted in the stem, making this option correct.

Cluster sampling is useful when a population is spread over a wide area and a complete list of individuals is difficult to obtain. The researcher first divides the population into natural groups such as schools or villages, randomly selects some clusters, and then studies all or a sampled subset of members within them.

Option A:
This option correctly outlines the two stage nature of cluster sampling, emphasizing both grouping into clusters and random selection of clusters for detailed study.

Option B:
This option describes convenience sampling, a nonprobability technique where individuals are chosen based on accessibility rather than structured clusters and random selection.

Option C:
Selecting only volunteers is an example of volunteer or self-selected sampling, which is nonprobability and does not involve random choice of clusters.

Option D:
Choosing experts intentionally because of their special knowledge is purposive sampling, another nonprobability method distinct from cluster sampling.

Stratified sampling divides the population into homogeneous subgroups or strata based on relevant characteristics such as gender, region or school type. Random samples are then drawn from each stratum. This approach increases precision and ensures that even small but important subgroups are properly represented, improving the quality of estimates.

Option A:
Option A wrongly states that stratification reduces coverage of subgroups, whereas its purpose is exactly to enhance subgroup coverage and representation.

Option B:
Option B is correct because it captures the main rationale: representing key strata in the sample in proportion or with specified emphasis. This helps in making subgroup comparisons and improves overall sample accuracy.

Option C:
Option C describes convenience sampling, which is nonprobability in nature and not related to stratification principles.

Option D:
Option D suggests avoiding random procedures, which contradicts the probabilistic nature of stratified sampling, where random selection operates within each stratum.