Parametric and Non Parametric methods differences,advantages and disadvantages

2. To determine
the range of
difference
STATISTICAL INFERENCE
Tests of
hypotheses

3. French Latin Greek
Parametre Parametrum Para =beside + Metron=measure
A value kept constant during an experiment, equation, calculation or similar,
but varied over other versions of the experiment, equation, calculation, etc.
Nonparametric methods is essentially concerned with the development of
statistical inference procedures without making any explicit assumption
regarding the functional form of the probability distribution of the sample
observations.
Parameter
Parametric statistical test is one that makes assumptions about the parameters
(defining properties) of the population distribution(s) from which one's data are
drawn

4. J. Wolfowitz in the 1942
The Annals of
Mathematical Statistics
THE ORIGIN OF THE WORD "NONPARAMETRIC"
The Assumption That Populations Have Distributions Of Known Functional Form . . .
[With] A Finite Number Of Parameters," While ". Methods ...
That Did Not Require Such Specialized Assumptions . Became Known As Non-parametric"
By 1967 The Predominant Spelling Had Changed To "Nonparametric," Which Had Meant

5. NONPARAMETRIC METHODS
IN COMPARISON TO
PARAMETRIC METHODS
Submitted by
Kishor Pujar
First Ph.D.
PALB 9014
Agricultural Entomology
University of Agricultural Sciences Bangalore

6. SEQUENCE OF PRESENTATION
Introduction
Nonparametric tests
Nonparametric methods Vs Parametric methods
Advantages and dis advantages of Nonparametric methods
Conclusion

7. Parametric
Methods
Set of fixed
parameters
Presumptions of
population
Population is
normal
Mean
Standard Deviation
Introduction

8. Distribution-
free method
Probability
law is
unspecified
No fixed set of
parameters
Doesn’t depend
on the
population
No assumption
on probability
distribution of
sample
Nonparametric methods

9. When to use Nonparametric methods
If data is not normal
Look at the
distribution of the
data
Normal Parametric
Not Nonparametric
Scale
Nominal or
ordinal
Nonparametric
Interval scales or
ratio scales
Parametric
If you want to test median rather than the mean
Look for scale

10. ONE SAMPLE
TESTS
TWO
RELATED
SAMPLE
TESTS
TWO
INDEPENDENT
SAMPLE TESTS
K RELATED
SAMPLE TESTS
K
INDEPENDENT
SAMPLE TESTS
Binomial test McNemar test Fishers Exact
probability test
Cochran Q test Chi-square test for
k independent
samples
Chi square one
sample test
Sign test Chi square test for two
independent samples
Friedman’s Two-
way ANOVA by
ranks
Extension of
Median test
Kolmogorov-
Smirnov one
sample test
Wilcoxon’s *
Matched pair
Signed rank test
Median test Kruskal-Wallis $
One-way ANOVA
by ranks
One sample runs
test
Mann-Whitney U test
#
Kolmogorov-Smirnov
Two sample test
Nonparametric tests
Alternative = * Paired samples t test, # Independent samples t test, $ one way ANOVA

11. MEASURES OF CORRELATION AND THEIR TESTS
1. CONTINGENCY COEFFICIENT (C )
2. PHI (Φ) COEFFICIENT FOR 2X2 TABLE.
3. CRAMER’S V COEFFICIENT
4. MANN KENDALL TREND TEST
5. SPEARMAN’S RANK CORRELATION COEFFICIENT (RS)
6. KENDALL’S RANK CORRELATION COEFFICIENT (Τ)
7. KENDALL’S COEFFICIENT OF CONCORDANCE (W)

12. Nonparametric tests Parametric tests
Applies to measurements such as
nominal, ordinal.
Require measurement equivalent to at least
an interval scale
Do not assume any parameters of the
parent population.
Assume some of the parameters of the
parent population.
Need more number of observations to
achieve the same size α
Need less number of observations to
achieve the size of α
Not systematic More systematic
It is to test medians It is to test group means.
It is applicable for both – Variable and
Attribute
It is applicable only for variables.
Nonparametric methods Vs Parametric methods

13. Nonparametric tests Parametric tests
It generally no assumptions about data.
It always considers strong assumptions about
data.
Requires much more data. Require lesser data
There is no assumed distribution Assumed to be a normal distribution.
Handle original data Handles – Intervals data or ratio data.
The result or outputs generated cannot be
seriously affected by outliers
The result or outputs generated can be easily
affected by outliers.
Its performance is at peak (top) when the
spread of each group is the same.
Its performance is at peak (top) when the
spread of each group is different.
Continued..

14. Provide exact probability statements
Useful when sample size is low (N=6)
Treating samples made up of observations
from several different populations.
Available to treat data which are inherently in
ranks as well as data whose numerical scores
have the strength of ranks.
Available to treat data which are in a nominal
scale
Advantages of
Nonparametric tests
.
The complexity is very low

15. Not useful, if the measurement is of the required strength,
• When all the assumptions of the parametric statistical model are in fact met in
the data
No Nonparametric methods for testing interactions in the analysis of variance
model,
• Unless special assumptions are made about additivity.
Disadvantages of Nonparametric tests

16. CONCLUSION
When we compare parametric and nonparametric methods, both are
having own role, significance and unique power efficiency in deriving
statistical inference.

17. THANK YOU