One of the hardest things in environmental statistics has been to test differences in group means when data are strongly skewed. Traditional t-tests and Analysis of Variance look for differences in means, but have low power when data are strongly skewed. Transformation to logarithms often addresses skewness, but the resulting tests on logs determine differences in geometric means, not testing for difference in means. Rank-based nonparametric tests look for differences in medians. What’s a scientist to do?
Our Permutation Test course introduces and explains how permutation methods resolve this problem. Permutation alternatives to t-tests, Analysis of Variance, and regression allow inferences about the mean to be made without assuming normality, and without transforming variables.
- What are the advantages of permutation tests over parametric tests?
- How do permutation tests work?
- Power of parametric vs nonparametric vs permutation tests
- Permutation tests for paired data
- Permutation tests for two groups (t-test situation)
- Permutation tests for 3+ groups (ANOVA situation)
- Permutation tests for correlation and regression
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