Interpreting basic statistical indicators – UGC NET Question

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.