Parametric and non-parametric tests differ in their assumptions about the population. Parametric tests assume the population is normally distributed and have equal variances, while non-parametric tests make no assumptions. Parametric tests are more powerful but require their assumptions to be met. Non-parametric tests are simpler and not affected by outliers. The document provides examples of common parametric and non-parametric tests for different study types such as comparing two or more groups or measuring the association between variables.

1. Parametric
VERSUS
Non Parametric test
BY JWANIKA VANSIYA

2. Parametric test
• A statistical test, in which specific assumptions
are made about the population parameter is
known as parametric test.
Parametric test

3. When to use parametric test?
Four conditions have to be satisfied:
When to use parametric test
????

4. .
Data should be normally distributed.

5. .
Variation in the results should be
roughly same..
• Homogenecity of variances assessed by
Levene’s test

6. Non parametric test
• Nonparametric tests are also called
distribution-free tests because they don’t
assume that your data follow a specific
distribution.
Non parametric test

7. When to use non parametric test?

8. Parametric test or Non parametric
test-Determination

10. 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

11. .
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

12. 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

13. 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

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