Statistical Analysis in Python - 363 Comparing Two Groups (Non Parametric)

Statistical Analysis in Python: Tutorial 5 – Comparing Two Groups with Non-Parametric Tests In this fifth tutorial of the Statistical Analysis in Python series, we shift our focus to non-parametric tests, ideal for situations where data does not meet the assumptions of normality. Building on our previous lesson on parametric methods, this video provides a detailed walkthrough of robust alternatives for comparing two groups. Topics covered: - When to use non-parametric tests - Mann-Whitney U test (alternative to independent t-test) - Wilcoxon signed-rank test (alternative to paired t-test) - Sign test for highly skewed paired data - Fisher’s exact test (alternative to chi-square) - Effect sizes for non-parametric tests - Comparing results from parametric vs. non-parametric approaches As always, we demonstrate concepts using Python code applied to the UCI Heart Disease Dataset. The first part includes a deep-dive explanation with slides and hand calculations using small datasets, helping you build a solid conceptual foundation before transitioning to real-world implementation in Python. Code: https://github.com/bnsreenu/python_for_microscopists/blob/master/363_Comparing_Two_Groups_Non_parametric_Tests.ipynb