probability

1. Parametri
c
VERSUS
Non Parametric
test

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

3. Four conditions have to be
satisfied:
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n
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ep
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aa
m
rae
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4. .
Data should be normally
distributed.

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

6. • Nonparametric tests are also called
distribution-free tests because they
don’t assume that your data follow a
specific distribution.
N
N
o
o
n
n
p
p
a
a
r
r
a
a
m
m
e
e
t
t
r
r
i
i
c
c
tteesstt

7. When to use non parametric
test?

8. Parametric test or Non
parametric test-
Determination

10. Parametric versus Non Parametric
test.
Parametric test Non parametric
test
Specific assumptions are
made regarding the
population
Parametric test is powerful if
it
is exists
Test statistics based
on distribution
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

15. Thank
you