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Parametric and
Nonparametric Test
By:
Sai Prakash
MBA Insurance
Management
Pondicherry University
∗ If the information about the population is completely
known by means of its parameters then statistical test is
called parametric test
∗ Eg: t- test, f-test, z-test, ANOVA
Parametric Test
∗ If there is no knowledge about the population or
paramters, but still it is required to test the hypothesis of
the population. Then it is called non-parametric test
∗ Eg: mann-Whitney, rank sum test, Kruskal-Wallis test
Nonparametric test
Classification Of hypothesis
Parametric test Non Parametric test
t- test, f-test, z-test, ANOVA
mann-Whitney, rank sum
test, Kruskal-Wallis test
Difference between parametric and Non
parametric
Parametric Non Parametric
Information about population is
completely known
No information about the population is
available
Specific assumptions are made regarding
the population
No assumptions are made regarding the
population
Null hypothesis is made on parameters of
the population distribution
The null hypothesis is free from
parameters
Difference between parametric and Non
parametric
Parametric Non Parametric
Test statistic is based on the distribution Test statistic is arbritary
Parametric tests are applicable only for
variable
It is applied both variable and artributes
No parametric test excist for Norminal
scale data
Non parametric test do exist for norminal
and ordinal scale data
Parametric test is powerful, if it exist It is not so powerful like parametric test
∗ Non parametric test are simple and easy to understand
∗ It will not involve complecated sampling theory
∗ No assumption is made regarding the parent population
∗ This method is only available for norminal scale data
∗ This method are easy applicable for artribute dates.
Advantages of non parametric test
∗ it can be applied only for norminal or ordinal scale
∗ For any problem, if any parametric test exist it is highly
powerful.
∗ Nonparametric methods are not so efficient as of
parametric test
∗ No nonparametric test available for testing the interaction
in analysis of variance model.
Disadvantages of non parametric test
Thank you....

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Parametric vs Nonparametric Tests

  • 1. Parametric and Nonparametric Test By: Sai Prakash MBA Insurance Management Pondicherry University
  • 2. ∗ If the information about the population is completely known by means of its parameters then statistical test is called parametric test ∗ Eg: t- test, f-test, z-test, ANOVA Parametric Test
  • 3. ∗ If there is no knowledge about the population or paramters, but still it is required to test the hypothesis of the population. Then it is called non-parametric test ∗ Eg: mann-Whitney, rank sum test, Kruskal-Wallis test Nonparametric test
  • 4. Classification Of hypothesis Parametric test Non Parametric test t- test, f-test, z-test, ANOVA mann-Whitney, rank sum test, Kruskal-Wallis test
  • 5. Difference between parametric and Non parametric Parametric Non Parametric Information about population is completely known No information about the population is available Specific assumptions are made regarding the population No assumptions are made regarding the population Null hypothesis is made on parameters of the population distribution The null hypothesis is free from parameters
  • 6. Difference between parametric and Non parametric Parametric Non Parametric Test statistic is based on the distribution Test statistic is arbritary Parametric tests are applicable only for variable It is applied both variable and artributes No parametric test excist for Norminal scale data Non parametric test do exist for norminal and ordinal scale data Parametric test is powerful, if it exist It is not so powerful like parametric test
  • 7.
  • 8. ∗ Non parametric test are simple and easy to understand ∗ It will not involve complecated sampling theory ∗ No assumption is made regarding the parent population ∗ This method is only available for norminal scale data ∗ This method are easy applicable for artribute dates. Advantages of non parametric test
  • 9. ∗ it can be applied only for norminal or ordinal scale ∗ For any problem, if any parametric test exist it is highly powerful. ∗ Nonparametric methods are not so efficient as of parametric test ∗ No nonparametric test available for testing the interaction in analysis of variance model. Disadvantages of non parametric test