UGC NET Questions (Paper – 1)

Statement C is wrong because statistical significance does not automatically imply practical importance; a small effect can be statistically significant in a large sample yet practically trivial. Statements A, B, D and E correctly describe the roles and potential misuses of statistics, including summarising data, the risk of misleading presentations, the need to report methods and the importance of understanding assumptions. Therefore, C alone is the incorrect statement in the list.

Option A:
Option A is correct because it isolates C as the sole misleading statement and acknowledges that the remaining statements align with good statistical practice. It emphasises the distinction between statistical and practical significance, which is crucial for sound interpretation.

Option B:
Option B is incorrect because it also labels A as wrong, even though A correctly explains that statistics help reveal patterns and relationships in data. Treating A as wrong underestimates the central purpose of statistical analysis.

Option C:
Option C is also wrong because it groups D with C, despite D rightly stressing transparency in reporting methods. Misclassifying D as wrong contradicts the principle of reproducible research.

Option D:
Option D is incorrect because it adds B and E to the set of wrong statements. Both B and E accurately warn about misleading presentations and the need to understand test assumptions, so including them with C misrepresents statistical best practices.

Statements A, B, D, E and F correctly explain aspects of significance testing, while C is wrong. Statement A is true because the null hypothesis typically asserts no difference or relationship, and statement B is correct that a small p-value suggests the data are unlikely under the null. Statement D rightly notes that not rejecting the null does not prove it true, and statement E is correct in pointing out that large samples can make small effects significant. Statement F is also true because practical interpretation requires attention to effect size and context, whereas C is false because statistical significance does not guarantee practical importance.

Option A:
Option A labels C and D as wrong, but D is actually a correct caution about interpreting non-rejection of the null. Misclassifying D as wrong makes this combination conceptually flawed.

Option B:
Option B treats A and C as wrong, but A is a standard definition of the null hypothesis and is therefore correct. By grouping A with the incorrect C, this option confuses fundamental concepts.

Option C:
Option C considers B and C wrong, but B is a correct interpretation of a small p-value in relation to the null hypothesis. Including B among wrong statements therefore invalidates this choice.

Option D:
Option D correctly isolates C as the only wrong statement and leaves all other statements intact as true. It distinguishes between statistical and practical significance, emphasising that significance alone does not ensure usefulness. Hence this combination is the correct answer.

Statements A, C, D and F describe key features of a good research problem. Statement A is true because a problem must be researchable given methods and resources. Statement C is correct since significance for theory or practice justifies the effort, and statement D is right that clear delimitation in terms of variables and context is necessary. Statement F is also true because many problems emerge from field experience or theoretical gaps, whereas B is false since the solution should not be known in advance and E is false because problems should be clearly, not vaguely, stated.

Option A:
Option A includes A, C and D, which are correct, but leaves out F. Since F correctly notes that research problems may arise from practice or theory gaps, omitting it makes the combination incomplete. A correct option must contain all the true statements identified in the list.

Option B:
Option B focuses on C, D and F and leaves out A. While C, D and F are true, A is also correct because feasibility in terms of methods and resources is an essential criterion. By excluding A, this option offers only a partial description of a good research problem.

Option C:
Option C correctly combines A, C, D and F and excludes the false statements B and E. It captures feasibility, significance, clear delimitation and genuine origin of problems, which together reflect sound research practice. Because it contains all and only the true statements, this is the correct answer.

Option D:
Option D brings in E alongside the otherwise correct statements. However, E is false because vagueness in problem statement leads to conceptual confusion and weak design. Including an incorrect statement makes this combination unacceptable.

Effect size quantifies how strong an association is or how large a difference is between groups, using metrics such as Cohen’s d, Pearson’s r or odds ratios. Unlike p-values, which are heavily influenced by sample size, effect sizes provide information about the practical or substantive importance of findings. They are crucial for interpreting the real-world impact of results and for planning future studies through power analysis. Because the stem emphasises a standardised measure of magnitude that is largely independent of sample size, effect size is the correct term.

Option A:
Effect size allows comparisons across studies using different scales by expressing differences or relationships in common metrics. For example, a large effect size suggests that an intervention has a meaningful impact beyond mere statistical significance. This focus on magnitude matches the description given in the stem.

Option B:
A p-value gives the probability of obtaining a result as extreme as or more extreme than the observed one, assuming the null hypothesis is true. It does not directly convey the size of the effect and can be small even for trivial effects if the sample is very large. Thus, p-value is not the best completion here.

Option C:
Standard error measures the variability of a sample statistic as an estimator of a population parameter and is used in constructing confidence intervals, but it is not itself a measure of the magnitude of a relationship or difference. Therefore, standard error is not the correct answer.

Option D:
Confidence level indicates the degree of certainty associated with confidence intervals, such as 95 percent, and reflects how often intervals built in the same way would capture the true parameter if sampling were repeated. It does not describe effect magnitude, so confidence level cannot complete the stem properly.