UGC NET Questions (Paper – 1)

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and E are correct: the sampling frame is the list from which samples are drawn, its quality affects representativeness, outdated frames cause coverage errors and an ideal frame covers the entire target population.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Option B is wrong because it groups C with D as wrong, even though C accurately states that outdated frames cause coverage errors.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.

A sampling frame is a practical list or representation of all units in the population that can potentially be selected for the sample, such as a roster of students or a registry of schools. It operationalises the population for sampling purposes. The quality of the sampling frame strongly affects the representativeness of the sample. Because the stem describes a complete list of sampling units from which the sample is drawn, sampling frame is the correct term.

Option A:
Population denotes the entire set of individuals or units to which the researcher wishes to generalise findings, but it may exist conceptually without a concrete list. A sampling frame is a specific listing used to select sample elements. Therefore, population does not precisely match the stem’s description.

Option B:
A sample is the subset of units actually selected from the sampling frame for study, not the list from which they are chosen. The stem focuses on the list itself, so sample is not the right completion.

Option C:
Sampling frames are crucial for probability sampling, as they determine whether every population element has a known chance of selection. Incomplete or biased frames can lead to coverage error. This role corresponds exactly to the description in the question, confirming sampling frame as the appropriate answer.

Option D:
Cluster refers to naturally occurring groupings within the population, such as schools or villages, which may be sampled in cluster sampling designs. It is not the same as the comprehensive list of units used in selection. Hence, cluster is not the correct term here.

In any research study, the population is the complete set of units about which the researcher wants to draw conclusions. Because studying all units is often impractical, a smaller group called a sample is selected from this population. The sample should represent the population so that inferences from sample data can be generalized appropriately. This distinction is fundamental to sampling theory.

Option A:
Option A incorrectly claims that population is always smaller than sample, which is logically impossible because a sample is drawn from the population. Samples are subsets and therefore cannot exceed the population in size.

Option B:
Option B clearly describes population as the full group of interest and the sample as a subset selected for actual study. This matches standard methodological definitions and underscores the logic of generalizing from sample to population.

Option C:
Option C states that sample and population are identical, which contradicts the very meaning of sampling. If they were identical, there would be no need to use sampling techniques.

Option D:
Option D incorrectly restricts the term “sample” to qualitative data and “population” to quantitative data. In reality, both populations and samples can involve any type of data; the distinction is based on whether we consider all units or only a subset.

A sampling frame operationalizes the target population by listing units that can potentially be included in the sample. Examples include voter lists, school registers or membership databases. The quality of the frame affects representativeness because omissions or duplications can bias selection. Thus, it is the practical source list from which sample units are chosen.

Option A:
Option A defines the population, which is the broader group of interest, not necessarily the actual list used for sampling.

Option B:
Option B is correct as it highlights the frame as the list or device from which sampling occurs. It connects the conceptual population to the operational step of sample selection.

Option C:
Option C refers to the achieved sample or respondents, which may be smaller than the selected sample due to nonresponse. This is not the sampling frame.

Option D:
Option D describes the questionnaire, which is a data collection tool, not the list of units.

A sampling frame is the operational list of all elements in the population that are accessible for selection. It could be a register, roster, database or list of households, depending on the study. A good frame should be as complete and accurate as possible to avoid coverage errors. Thus, the list from which the sample is drawn is properly called the sampling frame.

Option A:
A sampling unit is the basic element or group considered for selection, such as a person, household or school. It may be listed in the frame but is not itself the complete list. Therefore, it does not fully match the stem.

Option B:
Population refers to the entire set of units about which the researcher wishes to generalise. It is a conceptual aggregate and may not be directly available as an actual list. Hence, population is different from the specific list used for sampling.

Option C:
Sample size denotes the number of units selected for the study and does not refer to the list from which they are chosen. It is a numerical characteristic, not a register or roster. So this option is not correct here.

Option D:
Sampling frame specifically denotes the complete operational list of elements or clusters from which probability sampling procedures can be implemented. This is exactly what the question describes, making this option correct.

Statements A, B and C correctly describe population, sample, representativeness and sampling error, whereas D and E are wrong. Using a sample instead of a census does not automatically remove non-sampling error such as measurement or non-response error. Similarly, simply increasing sample size cannot eliminate bias caused by an incomplete or faulty sampling frame. Therefore, the wrong statements are D and E together.

Option A:
Option A identifies only D as wrong and overlooks E. Although D is indeed incorrect, E also contains a misconception by claiming that large samples remove frame-induced bias. Because it fails to include all wrong statements, this option is incomplete.

Option B:
Option B isolates only E as wrong and ignores D. While E is clearly incorrect, D also misleads by implying that sampling automatically eliminates non-sampling error. Hence, Option B does not capture the full set of wrong statements.

Option C:
Option C is correct because it groups D and E, the two statements that misrepresent what sampling can achieve. It recognises that non-sampling errors and frame bias require other remedies such as better instruments, careful procedures and accurate frames.

Option D:
Option D incorrectly includes C among the wrong statements. Statement C is actually a standard definition of sampling error as the difference between a statistic and the corresponding parameter. Misclassifying this correct definition makes the combination invalid.

Option E wrongly presents A, which correctly defines a sample, as a wrong statement alongside D and E. By treating a basic concept as incorrect, this option cannot be accepted.

Systematic sampling involves arranging the population in some order, choosing a random starting point and then selecting every kth element, where k is the sampling interval determined by the ratio of population size to desired sample size. This method is simpler to implement than simple random sampling while still maintaining an element of randomness. Because the stem explicitly mentions selecting every kth element after a random start, it describes systematic sampling.

Option A:
Cluster sampling focuses on selecting entire groups or clusters, such as schools or classes, often for reasons of cost and convenience. It does not rely on selecting every kth element from a list, so it does not match the feature described in the question.

Option B:
Systematic sampling ensures that the sample is spread relatively evenly across the ordered list, which can be advantageous when the list has no hidden periodicity that coincides with the sampling interval. The process of choosing a random start and then every kth case corresponds exactly to the stem’s description, confirming systematic as the correct answer.

Option C:
Quota sampling is a non-probability method in which the researcher fills pre-specified quotas for particular subgroups, often using convenience within each subgroup. It does not involve selecting elements at a fixed interval from a list and does not rely on random starts. Therefore, quota is not an appropriate answer here.

Option D:
Purposive sampling involves deliberately choosing cases judged to be particularly informative rather than following a fixed interval rule. It is commonly used in qualitative research and does not follow the mechanical pattern described in the stem. Hence, purposive sampling is not correct for this question.