Statistical significance and hypothesis testing – UGC NET Question

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.