This document summarizes the key differences between parametric and non-parametric tests. Parametric tests make specific assumptions about the population, such as the data following a normal distribution. They are more powerful but only apply if their assumptions are met. Non-parametric tests make no assumptions about the population and can be used on nominal scale data, but are generally less powerful. The document provides examples of common parametric and non-parametric tests used for different study types such as comparing two or more groups.

1. Parametric
VERSUS
Non Parametric test
BY A.Rachana
16331E005

2. • A statistical test, in which specific assumptions are
made about the population parameter is known as
parametric test.
Parametric test
Non parametric test
• Nonparametric tests are also called distribution-free
tests because they don’t assume that your data
follow a specific distribution.

3. Parametric versus Non Parametric test.
Parametric test
Specific assumptions are made
regarding the population
Parametric test is powerful if it
is exists
Test statistics based on
distribution
Non parametric test
No specific assumptions are
made regarding the population
Not powerful like parametric
test
Test statistics is arbitrary

4. .
Parametric test
No parametric test exists for
nominal scale data
Central measure - mean
Can draw more conclusions
Non parametric test
Non parametric test exists
for nominal scale data
Central measure - median
Simplicity , not affected by
outliers

5. Parametric versus non parametric test
Study type Parametric test Non
parametric
test
Compare means between two
distinct/independent groups
Two-sample t-test Mann-
whitney test
Compare two quantitative
measurements taken from the
same individual
Paired t-test Wilcoxon
signed-rank
test
Compare means between
three or more
distinct/independent groups
Analysis of variance (ANOVA) Kruskal-
Wallis test

6. Study type Parametric test Non parametric test
Repeated measures, >2
conditions
One-way, repeated
measures ANOVA
Friedman's test
Estimate the degree of
association between two
quantitative variables
Pearson coefficient of
correlation
Spearman’s rank
correlation
Parametric versus non parametric test