Effect Sizes and Statistical Significance › Summary
Michael BrenndoerferJanuary 8, 20261 min read
Machine Learning from ScratchAll chapters Part 2 of 9Hypothesis Testing
Effect Sizes and Statistical Significance › SummaryOne section · Sections in this chapter9 / 11 ↓
Sections in this chapter11
Reading tools Definitions & inline links
Choose your expertise level to adjust how many terms are explained. Beginners see more tooltips, experts see fewer to maintain reading flow. Hover over underlined terms for instant definitions.
Article links
Make inline references clickable
Effect sizes complement p-values by quantifying the magnitude of effects rather than merely establishing their existence:
Cohen's d measures standardized mean differences:
- Small: d ≈ 0.2
- Medium: d ≈ 0.5
- Large: d ≈ 0.8
Variants exist for different situations:
- Hedges' g for small samples
- Glass's Δ for unequal variances
- Cohen's d_z for paired samples
Other effect sizes:
- η² and ω² for ANOVA (proportion of variance)
- r for correlations
- Cohen's h and odds ratios for proportions
Key principles:
- Statistical significance ≠ practical significance
- Large samples can detect trivial effects
- Always interpret effect sizes in context
- Report confidence intervals when possible