Effect Size Calculator & Interpreter
Overview
The Effect Size Calculator helps you calculate and interpret multiple effect size measures for different types of data and statistical tests. Unlike p-values, which only tell you whether an effect exists, effect sizes tell you how large or important that effect is. This interactive tool calculates Cohen’s d, Pearson’s r, eta-squared, Cramér’s V, odds ratios, and risk ratios, providing interpretations and visualizations to help you understand the practical significance of your findings.
Tips
- Effect sizes quantify the magnitude of a difference or strength of a relationship
- Cohen’s d: standardized mean difference (d = 0.2 small, 0.5 medium, 0.8 large)
- Pearson’s r: correlation coefficient (-1 to +1, with 0.1, 0.3, 0.5 as small/medium/large)
- Eta-squared (η²): proportion of variance explained in ANOVA (0.01, 0.06, 0.14 benchmarks)
- Cramér’s V: association strength for categorical data (depends on df)
- Odds ratio and risk ratio: compare probabilities between groups (>1 means increased risk)
- Effect sizes are independent of sample size (unlike p-values!)
- Always report effect sizes along with p-values for complete interpretation
- Consider field-specific benchmarks - Cohen’s guidelines are just starting points
- Confidence intervals around effect sizes show precision of the estimate