Data scientists apply robust statistics with Python's Pingouin for messy data
Updated
Updated · KDnuggets · May 1
Data scientists apply robust statistics with Python's Pingouin for messy data
6 articles · Updated · KDnuggets · May 1
The article demonstrates three methods on a wine-quality dataset: Mann-Whitney U, Wilcoxon signed-rank and Welch's ANOVA, after normality or equal-variance assumptions fail.
Examples found no significant alcohol-content difference between red and white wines, but significant differences in acidity measures and residual sugar across quality ratings.
It argues robust, rank-based or variance-adjusted tests can produce more reliable results than classical t-tests or ANOVA when real-world data are skewed, noisy or contain outliers.
When messy data breaks multiple rules, which robust test is the most reliable choice?
Do robust statistics risk making data scientists complacent about essential data cleaning?
How can robust statistics safeguard complex AI models from amplifying hidden data flaws?