About Effect Sizes

Effect size is a quantitative measure of the magnitude of a phenomenon. Unlike p-values, effect sizes are independent of sample size and provide meaningful information about the practical significance of your findings.

Common Effect Size Measures:

• Cohen's d: Standardized mean difference (t-tests)
• Eta-squared (η²): Proportion of variance explained (ANOVA)
• Omega-squared (ω²): Unbiased estimate of variance (ANOVA)
• Cramer's V: Association strength (Chi-square tests)
• Pearson r: Correlation strength

Why Effect Sizes Matter:

• Required for meta-analyses and systematic reviews
• Essential for power analysis and sample size planning
• Helps distinguish statistical from practical significance
• Allows comparison across different studies and measures