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Learn non-parametric statistical tests for comparing two groups when data doesn't meet normality assumptions in this 45-minute tutorial from the Statistical Analysis in Python series. Master the Mann-Whitney U test as an alternative to independent t-tests, the Wilcoxon signed-rank test for paired data, the sign test for highly skewed distributions, and Fisher's exact test as a chi-square alternative. Explore when to choose non-parametric over parametric methods, calculate effect sizes for non-parametric tests, and compare results between both approaches. Follow along with Python implementations using the UCI Heart Disease Dataset, starting with conceptual explanations through slides and hand calculations on small datasets before progressing to real-world coding applications.
