UGC NET Questions (Paper – 1)

A confidence interval provides a range of plausible values for a population parameter based on sample data and a chosen confidence level, such as 95 percent. It accounts for sampling variability and offers more information than a single point estimate. Interpreting the interval helps researchers understand the precision of their estimates and the uncertainty surrounding them. Therefore, the interval described in the stem is correctly called a confidence interval.

Option A:
Confidence intervals are built using the sampling distribution of the statistic and reflect both sample size and variability. A narrower interval indicates more precise estimation, whereas a wider one signals greater uncertainty. These properties align directly with the description of an interval likely to contain the true parameter.

Option B:
A p-value indicates the probability of obtaining results as extreme as those observed, assuming the null hypothesis is true, and is used for hypothesis testing rather than interval estimation. It is not itself an interval around a statistic. Hence, p-value is not the correct completion.

Option C:
Effect size quantifies the magnitude of a relationship or difference but does not by itself convey the sampling uncertainty around the estimate. It can be accompanied by a confidence interval, but it is conceptually distinct. Therefore, effect size is not appropriate here.

Option D:
A test statistic is a single calculated value, such as a t or F value, used to decide whether to reject the null hypothesis, not an interval within which a parameter is likely to lie. Thus, test statistic is not the term being defined in the stem.

Statement D is wrong because a p-value does not directly give the probability that the null hypothesis is true; it is the probability of obtaining results as extreme as those observed, assuming the null is true. Statements A, B, C and E are correct: inferential methods generalise from samples, significance depends on several factors, confidence intervals express plausible parameter ranges and context plus effect size are vital for interpretation. Thus, only D is the incorrect statement.

Option A:
Option A is incorrect because it also labels A as wrong, despite A correctly explaining the purpose of inferential statistics. Disputing A blurs the distinction between sample-level and population-level reasoning.

Option B:
Option B is correct because it isolates D as the single misinterpretation, while implicitly confirming the other statements as accurate. It emphasises that the p-value is often misunderstood and must be interpreted cautiously.

Option C:
Option C is wrong because it treats C, which accurately describes confidence intervals, as wrong along with D. This misrepresents a core concept in inferential statistics.

Option D:
Option D is incorrect because it declares B and E wrong along with D, even though both B and E reflect best practice for interpreting statistical results by considering multiple determinants and substantive meaning.