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DIstinguish between Parametric vs nonparametric test
This document summarizes parametric and nonparametric tests. Parametric tests make assumptions about the population based on known parameters, while nonparametric tests make no assumptions about the population. Some examples of parametric tests provided are t-test, F-test, z-test, and ANOVA, while examples of nonparametric tests include Mann-Whitney, rank sum test, and Kruskal-Wallis test. The key differences between parametric and nonparametric tests are that parametric tests are based on population parameters and distributions while nonparametric tests are not, and parametric tests can only be applied to variable data while nonparametric tests can be used for variable or attribute data.
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Presentation by Sai Prakash on classification of parametric and nonparametric tests.
Parametric tests require complete knowledge of population parameters; examples include t-test, f-test, z-test, and ANOVA.
Nonparametric tests do not require knowledge of population parameters; examples include Mann-Whitney, rank sum, and Kruskal-Wallis tests.
Classifies statistical tests into parametric (t-test, f-test, etc.) and nonparametric (Mann-Whitney, Kruskal-Wallis, etc.) categories.
Key differences: Parametric tests use population parameters; nonparametric tests don't. Parametric tests assume distribution, while nonparametric tests can apply to nominal data.
Nonparametric tests are simpler, involve less complex sampling theory, and require no assumptions about population.
Nonparametric tests are less powerful than parametric tests and have limitations in testing interactions within ANOVA models.
Outro and thanks expressed by the presenter.
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DIstinguish between Parametric vs nonparametric test
- 1. Parametric and Nonparametric Test By: SaiPrakash MBA Insurance Management Pondicherry University
- 2. ∗ If theinformation 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 thereis 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 Parametrictest Non Parametric test t- test, f-test, z-test, ANOVA mann-Whitney, rank sum test, Kruskal-Wallis test
- 5. Difference between parametricand 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 parametricand 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
- 8. ∗ Non parametrictest 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 canbe 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
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