UGC NET Questions (Paper – 1)

Parametric tests operate under assumptions about the parameters of the population distribution, typically including normality of the dependent variable and homogeneity of variances across groups. They are powerful when these assumptions are reasonably met and are widely used for interval or ratio data. Examples include t-tests and analysis of variance. Therefore, tests that rely on such distributional assumptions are correctly termed parametric tests.

Option A:
Non-parametric tests do not require strict assumptions about normal distribution or equal variances and are often used for ordinal data or when parametric assumptions are violated. They include tests such as Mann–Whitney U and Kruskal–Wallis, so they do not match the stem.

Option B:
Descriptive statistics summarise data through measures like mean, median and standard deviation but are not themselves inferential tests requiring distributional assumptions. Thus, descriptive is not the appropriate label here.

Option C:
Option C, parametric, emphasises that these tests estimate and compare population parameters such as means under certain model assumptions. Because the stem cites t-tests and ANOVA as examples, this term fits perfectly.

Option D:
The chi-square test is generally considered a non-parametric test used for categorical data, not a representative example of parametric testing. Therefore, chi-square is not the correct completion.