Parametric Tests 1) Assumption of population normality 2) homogeneity of variance Parametric more powerful than nonparametric.

Parametric Tests

1) Assumption of population normality

2) homogeneity of variance

Parametric more powerful than nonparametric

Nonparametric TestsNonparametric Test (distribution free

testing)1) Pathological conditions are represented by

skewed distributions2) Small clinical samples and samples of

convenience cannot be considered representatives of larger nominal distributions

3) Data measured on nominal - category labels ordinal - rank order of observations

Non Parametric Parametric

Mann-Whitney U TestRank

Unpaired T Test

Non Parametric Parametric

Sign TestWilcoxon Signed

Ranks Test

Paired T Test

Non Parametric Parametric

Kruskal Wallis One-Way Analysis of Variance by Ranks

ANOVA F Test

Non Parametric Parametric

Friedman Two-way Analysis of Variance by Ranks

RM ANOVA

Chi Square X2

Non parametric test for analyzing categorical data e.g. yes-no Frequency of occurrenceDetermines if there is a difference between the proportions observed within a set of categories and the proportions that would be expected by chance.

Assumptions

1) frequencies represent individual counts

2) Categories are exhaustive and mutually exclusive