UGC NET Questions (Paper – 1)

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.

Sampling error is the random difference between values obtained from a sample and the true values in the population. It occurs because the sample, even if selected properly, represents only a subset of the population. As a result, estimates like means or proportions fluctuate from sample to sample. Larger and more representative samples reduce sampling error but cannot eliminate it entirely.

Option A:
Option A describes errors arising from mistakes in coding or tabulation, which are classified as non sampling errors. These can occur even in a census where the whole population is measured.

Option B:
Option B correctly explains sampling error as the natural variation due to observing only a part of the population. It captures the essence of random fluctuation inherent in the sampling process and is consistent with statistical theory.

Option C:
Option C refers to deliberate response distortion or bias, which again falls under non sampling errors, specifically response bias. While serious, it is conceptually distinct from sampling error.

Option D:
Option D deals with faulty instruments, another form of non sampling error because it affects measurement irrespective of whether a sample or census is used.

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.

Statements A, B, C and D distinguish sampling error from non-sampling error, and F highlights a way to reduce some non-sampling problems. A is true because sampling error results from studying only a subset, while B correctly notes that poor instruments create non-sampling error. C is accurate in classifying non-response bias as a non-sampling error, and D correctly observes that larger samples usually reduce sampling error. F is also true since properly trained field staff can lower interviewer and recording errors, whereas E is false because random sampling addresses sampling error, not all forms of non-sampling error.

Option A:
Option A ignores statement F, which draws attention to practical steps for reducing non-sampling error through training. While A, B, C and D are correct, the omission of F makes the description less complete in terms of remedies. Thus, this option is not fully correct.

Option B:
Option B adds statement E, falsely claiming that non-sampling errors can be entirely removed by random sampling. Random sampling influences only sampling error; measurement, non-response and recording errors can still occur. Because it includes a false statement, this option is invalid.

Option C:
Option C is correct because it lists A, B, C, D and F, capturing both definitions and some methods for error reduction. It properly excludes E, which overstates the power of random sampling. Therefore, this combination contains all and only the true statements.

Option D:
Option D omits statement A and includes E, thereby losing the basic definition of sampling error and adding an incorrect assertion. This combination confuses the distinction between error types and cannot be accepted.

Sampling error refers to the natural discrepancy between sample statistics and population parameters that arises because only part of the population is observed. Even with perfectly random sampling and accurate measurement, different samples will yield slightly different estimates. This variability is quantified in terms such as standard error and confidence intervals. Hence, the difference described in the stem is correctly called sampling error.

Option A:
Measurement error occurs when the instruments or procedures used do not yield perfectly accurate values, leading to inaccuracies that are distinct from the randomness of sampling. It is related to how variables are measured rather than to the fact that only a subset is observed. Therefore, measurement error is not the best completion.

Option B:
Systematic error is a consistent bias in measurement or procedure that pushes results in one direction, such as miscalibration or interviewer bias. It does not average out across samples and is not due simply to using a subset. Consequently, systematic error is not what the stem is defining.

Option C:
Sampling error arises even in well designed probability samples and can be estimated and controlled by appropriate sample sizes. It reflects sample-to-sample fluctuation, making it the correct term for the discrepancy between sample and population values mentioned in the question.

Option D:
Non-response error occurs when individuals who fail to respond differ systematically from those who do, introducing bias, but this is a specific non-sampling error, not the inherent fluctuation due to partial observation. Thus, non-response error is not the accurate completion.

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.

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.