The following are characteristics of non-parametric tests: They

(A) do not suppose any particular distribution

(B) are quick and easy to use

(C) cannot be applied to nominal and ordinal scales

(D) can be used when measurements are not very accurate

(E) make assumptions about homogeneity of variance

Choose the correct answer from the options given below:

Correct option is B

Non-parametric tests are statistical methods that do not assume any specific distribution for the data, making them useful for analyzing data that does not meet the assumptions required for parametric tests. Here’s how the options apply:
1. Do not suppose any particular distribution (A):
· Non-parametric tests do not assume that the data follows a particular distribution (such as normal distribution), making them flexible in handling various data types, including those with skewed distributions.
2. Are quick and easy to use (B):
· Non-parametric tests are simple to apply, especially when data is non-normal or when dealing with ordinal or nominal data. They often require less computation than their parametric counterparts.
3. Can be used when measurements are not very accurate (D):
· Non-parametric tests are robust and can be applied even when the measurements are not precise or have errors. They do not rely on exact measurements and are ideal for ranked or approximate data.
Information Booster:
· Common non-parametric tests include the Mann-Whitney U test, Kruskal-Wallis test, Chi-square test, and Wilcoxon signed-rank test. These are widely used when the assumptions for parametric tests (such as normality and homogeneity of variance) are not met.
Additional Knowledge:
· Cannot be applied to nominal and ordinal scales (C) – Incorrect:
· Non-parametric tests are specifically designed for use with nominal and ordinal scales. For example, Chi-square tests are used for nominal data, and Kruskal-Wallis tests for ordinal data.
· Make assumptions about homogeneity of variance (E) – Incorrect:
· Non-parametric tests do not assume homogeneity of variance, which is one of the key assumptions of parametric tests like ANOVA. They are more flexible and do not require equal variances across groups.