NormalityTests
Performs multiple normality tests on numerical features to assess distribution normality.
Purpose
This test evaluates whether numerical features follow a normal distribution using various statistical tests. Understanding distribution normality is crucial for selecting appropriate statistical methods and model assumptions.
Test Mechanism
The test applies multiple normality tests: - Shapiro-Wilk test: Best for small to medium samples - Anderson-Darling test: More sensitive to deviations in tails - Kolmogorov-Smirnov test: General goodness-of-fit test
Signs of High Risk
- Multiple normality tests failing consistently
- Very low p-values indicating strong evidence against normality
- Conflicting results between different normality tests
Strengths
- Multiple statistical tests for robust assessment
- Clear pass/fail indicators for each test
- Suitable for different sample sizes
Limitations
- Limited to numerical features only
- Some tests sensitive to sample size
- Perfect normality is rare in real data