UGC NET Questions (Paper – 1)

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.

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, B, C and E correctly describe basic statistical ideas, while D is false. The mean is influenced by extreme values, the median resists them better, standard deviation measures dispersion and in a normal distribution the three central tendency measures coincide. Effect size indeed reflects how large a difference or association is, complementing significance tests. However, a small p-value does not necessarily imply a large effect; with a big sample even tiny effects can be statistically significant.

Option A:
Option A omits E, ignoring the important interpretive role of effect size in judging the practical importance of findings. Without E, the explanation remains focused only on descriptive statistics and neglects the link to substantive significance.

Option B:
Option B leaves out B, so it fails to mention standard deviation, a key descriptor of variability, even though it comments on mean, median and effect size. This omission makes the option incomplete.

Option C:
Option C excludes A, thereby not recognising the difference in sensitivity to outliers between mean and median, which is a core interpretive issue. As a result, it cannot be accepted.

Option D:
Option D is correct because it combines all true statements about measures of central tendency, variability and effect size, while rejecting D, which mistakenly equates small p-values with large effects. It encourages a nuanced interpretation of statistical results.

Option E contains D, the false claim that a small p-value guarantees a large effect size, and so any combination including it becomes incorrect, even if other parts are true.