Correct option is B
Non-parametric tests are statistical tests that do not assume a specific distribution for the data and are often used when the data is ordinal, or when the assumptions of parametric tests (like normality) are not met. The following are
non-parametric tests:
1.
A. Mann Whitney U test: This is a non-parametric test used to compare differences between two independent groups when the dependent variable is ordinal or continuous but not normally distributed.
2.
B. Sign test: This is a non-parametric test used to test the median of a population or to compare two related samples when data is ordinal or not normally distributed.
3.
C. Kruskal-Wallis H test: This is a non-parametric alternative to the
one-way ANOVA and is used to compare more than two independent groups when the dependent variable is ordinal or not normally distributed.
The tests
D. t-test and
E. ANOVA are
parametric tests, which require the assumption of normality in the data and are used when the data is continuous and normally distributed.
Information Booster:
·
Mann Whitney U test (A) is used to compare the medians between two independent groups, making it ideal for non-parametric data.
·
Sign test (B) is used to test hypotheses regarding the median or compare two related samples, particularly when the data is skewed or not normally distributed.
·
Kruskal-Wallis H test (C) is used for comparing more than two independent groups when assumptions of normality for ANOVA are not met.
Additional Knowledge on Incorrect Options:
·
t-test: This is a
parametric test used to compare the means of two groups. It assumes that the data is normally distributed and typically used with interval or ratio data.
·
ANOVA: This is a
parametric test used to compare the means of three or more groups, assuming normality of the data and homogeneity of variance.
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