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.

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.

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.

The power of a statistical test is the probability that it will detect an effect and reject the null hypothesis when the null is actually false. High power means a lower chance of committing a Type II error, or false negative. Power is influenced by factors such as sample size, effect size, variability in the data and the chosen significance level. Because the stem describes the probability of correctly rejecting a false null hypothesis, it is defining the power of the test.

Option A:
Significance level, often denoted by alpha, is the probability of committing a Type I error—that is, rejecting a true null hypothesis. It sets the threshold for how much risk of a false positive the researcher is willing to accept. Since the stem is about correctly rejecting a false null, significance level is not the correct answer.

Option B:
Effect size quantifies the magnitude of a relationship or difference, providing information about practical importance rather than decision error probabilities. While larger effect sizes generally increase power, effect size itself is not the probability of correct rejection. Thus, effect size does not fit the description in the question.

Option C:
Confidence level refers to the proportion of times that confidence intervals constructed from repeated samples would contain the true population parameter. It is related to estimation rather than hypothesis testing power. Therefore, confidence level is not the term that describes the probability of correctly rejecting a false null hypothesis.

Option D:
Power reflects the sensitivity of a test to detect real effects, guiding researchers in planning sample sizes to avoid underpowered studies that may miss meaningful results. Given that the stem explicitly refers to correctly rejecting a false null hypothesis, power is the appropriate completion.

Statements A, C and D are correct, while B and E are false. Large, well-distributed samples support stronger generalisations, and anecdotes from one or two cases provide weak support for universal claims. Over-generalisation from limited data is a frequent pattern in exam questions, so D is also true. A biased sample cannot be treated as perfectly representative, and inductive support is not judged in the strict valid/invalid sense of deduction, which makes B and E incorrect. Therefore A, C, D only is the correct set.

Option A:
Option A is incomplete as it omits D, ignoring the explicit exam link to over-generalisation that the question mentions. A and C only therefore does not fully respond to the stem.

Option B:
Option B is correct as it groups together all true claims (A, C and D) while excluding B and E, which distort the nature of inductive reasoning. This matches logical reasoning strategies used for UGC NET.

Option C:
Option C is incorrect since it treats E as true even though inductive generalisations are usually graded by strength, not by the binary valid/invalid scheme. C, D, E only thus misclassifies a false statement as correct.

Option D:
Option D is wrong because it includes B along with A, C and D; however B is false because a biased sample cannot be assumed representative. A, B, C, D only therefore includes an incorrect statement.

Statistical power quantifies the sensitivity of a test to identify real effects when they exist. It is mathematically equal to one minus beta, where beta is the probability of a Type II error. High power means that the test is unlikely to miss true differences or relationships. Therefore, the probability described in the stem is called the power of the test.

Option A:
Precision refers to how close repeated estimates are to one another, such as narrow confidence intervals, but it is not the standard term for the probability of detecting true effects.

Option B:
Power is influenced by factors such as sample size, effect size, significance level and variability in the data. Researchers often conduct power analyses while designing studies to ensure that they have a reasonable chance of finding meaningful effects. This focus on correctly rejecting false null hypotheses matches the stem, making this option correct.

Option C:
Reliability concerns the consistency of measurements over time or across items and is a property of instruments, not directly of hypothesis tests.

Option D:
Validity relates to the appropriateness of inferences drawn from test scores or study designs; it does not specifically denote the probability of detecting true effects in statistical testing.