364 Comparing Multiple Groups with ANOVA

Statistical Analysis in Python: Tutorial 6 – Comparing Multiple Groups with ANOVA In this sixth tutorial of the Statistical Analysis in Python series, we move beyond comparing two groups and explore how to compare three or more groups using ANOVA (Analysis of Variance). This session explains why ANOVA is preferred over running multiple t-tests and provides a clear understanding of its theory and practical application. Topics covered: - Why ANOVA instead of multiple t-tests? - One-way ANOVA fundamentals - F-statistic and F-distribution - Assumptions and diagnostics for ANOVA - Post-hoc testing with Tukey's HSD - Effect sizes: Eta squared (η²) and Omega squared (ω²) - Non-parametric alternative: Kruskal-Wallis test We use the Palmer Penguins Dataset to demonstrate the full workflow, from concept to Python code. The tutorial begins with a deep-dive explanation using slides, including hand calculations on simplified datasets, followed by real-world implementation in Python. Code: https://github.com/bnsreenu/python_for_microscopists/blob/master/364_Comparing_Multiple_Groups_ANOVA.ipynb