What are the differences between ANOVA and Kruskal-Wallis? When would you use one test over the other?
Understand the Problem
The question is asking for a comparison between two statistical tests: ANOVA and Kruskal-Wallis. It seeks an explanation of the contexts in which each test is appropriate to use, highlighting the conditions such as normality of the dependent variable, and the parametric versus non-parametric nature of these tests.
Answer
ANOVA requires normally distributed data; Kruskal-Wallis does not.
ANOVA is used for comparing means across groups but assumes normal distribution and homoscedasticity. Kruskal-Wallis is a non-parametric alternative used when the data does not meet ANOVA assumptions, such as normal distribution or equal variances.
Answer for screen readers
ANOVA is used for comparing means across groups but assumes normal distribution and homoscedasticity. Kruskal-Wallis is a non-parametric alternative used when the data does not meet ANOVA assumptions, such as normal distribution or equal variances.
More Information
ANOVA is parametric, assuming normally distributed and homoscedastic data, suited for comparing means. Kruskal-Wallis is non-parametric and compares median ranks among groups, useful when data does not meet normality assumptions.
Tips
A common mistake is using ANOVA when data is not normally distributed, leading to invalid results. Always check assumption criteria before selecting a test.
Sources
- What are the differences between ANOVA and Kruskal-Wallis? - library.virginia.edu
- Difference Between ANOVA and Kruskal-Wallis test - Cross Validated - stats.stackexchange.com
- When to Use Kruskal-Wallis Instead of ANOVA in Statistical Analysis - isixsigma.com
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