Non Parametric Test: PDF / PPT
Download PDF, notes, and PPT related to Non Parametric Tests. This resource provides comprehensive material for understanding non-parametric statistical tests, their assumptions, and applications in data analysis.
Keywords: download pdf, notes, ppt, non-parametric tests, Mann-Whitney U test, Wilcoxon signed-rank test, Kruskal-Wallis test, statistical methods
Non Parametric Tests: A Detailed Explanation
Non-parametric tests are statistical methods that do not rely on assumptions about the population parameters or the underlying distribution of the data. These tests are particularly useful when the data does not meet the assumptions required for parametric tests, such as normality or homogeneity of variance.
Some of the most commonly used non-parametric tests include:
- Mann-Whitney U Test: Used to compare differences between two independent groups when the dependent variable is either ordinal or continuous but not normally distributed.
- Wilcoxon Signed-Rank Test: Used to compare two related samples or repeated measurements on a single sample to assess whether their population mean ranks differ.
- Kruskal-Wallis Test: A non-parametric alternative to ANOVA, used to compare three or more independent groups when the dependent variable is ordinal or not normally distributed.
- Chi-Square Test: Used to test the association between categorical variables or to compare observed frequencies with expected frequencies.
Non-parametric tests are robust and flexible, making them suitable for a wide range of data types and research scenarios. However, they may have less statistical power compared to parametric tests when the assumptions of parametric tests are met. Understanding the appropriate use of non-parametric tests is essential for accurate and reliable statistical analysis.
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