UGC NET Questions (Paper – 1)

Parametric tests, such as t tests and ANOVA, rely on certain assumptions about the population distribution and measurement scale. Typically, they assume that the underlying population is approximately normal and that the data are measured on interval or ratio scales. When these assumptions are reasonably met, parametric tests are powerful tools for detecting differences and relationships.

Option A:
Option A describes conditions more suitable for nonparametric tests, which handle nominal data and skewed distributions better. Parametric procedures would often be invalid in such cases.

Option B:
Option B correctly states the usual requirements for parametric testing, including approximate normality and higher level measurement scales. Under these conditions, parametric tests provide efficient and interpretable inferences.

Option C:
Option C claims that no assumptions can be made about the population distribution, which contradicts the very nature of parametric methods. Their validity depends on assumed distributions and other conditions like homogeneity of variance.

Option D:
Option D focuses on extremely small samples and unequal categories, again a context where nonparametric tests or exact methods might be preferred.

Parametric tests are inferential statistical procedures that rely on specific assumptions about population parameters and distributions, such as normality of the underlying distribution and homogeneity of variance. They typically require data measured at the interval or ratio level and can be more powerful than non-parametric tests when their assumptions are met. Examples include t-tests and ANOVA. Because the stem mentions known distribution and interval or ratio data, it is describing a parametric test.

Option A:
Non-parametric tests are used when data violate assumptions required for parametric tests or when measurement scales are nominal or ordinal. They make fewer distributional assumptions and are sometimes called distribution-free. Since the stem explicitly refers to assuming a known distribution, non-parametric is not correct.

Option B:
Distribution-free is another term for non-parametric tests, indicating that they do not presume a particular form of the population distribution. This is opposite to the assumption highlighted in the stem, so distribution-free cannot complete the statement accurately.

Option C:
Chi-square is a specific non-parametric test used mainly for categorical data, not a general label for tests assuming interval or ratio data and known distributions. Although it is important in research, it does not fit the definition of parametric testing provided in the question.

Option D:
Parametric tests exploit distributional information to conduct more sensitive analyses under appropriate conditions, often providing narrower confidence intervals and higher power. The reliance on interval or ratio data and assumptions about the population aligns precisely with the stem’s description, confirming parametric as the right completion.