Effect Size Calculator
Calculate and interpret effect sizes for your research with this comprehensive free calculator. Supports Cohen's d for comparing group means, Hedges' g for small-sample corrections, eta-squared (η²) and omega-squared (ω²) for ANOVA designs, Pearson's r for correlations, odds ratios, and Cohen's w for chi-square tests. Perfect for meta-analysis, dissertation statistics, grant proposals, and any research requiring standardized effect size reporting. Includes interpretation guidelines, confidence intervals, and conversion between effect size metrics. No registration required.
Key Features
- Cohen's d effect size calculation
- Hedges' g with small-sample correction
- Eta-squared (η²) for ANOVA
- Omega-squared (ω²) for ANOVA
- Pearson r effect size
- Cohen's w for chi-square tests
- Odds ratio calculation
- Glass's delta effect size
- Effect size interpretation guidelines
- Confidence interval estimation
- Conversion between effect size metrics
- Meta-analysis effect size support
- Practical significance interpretation
- Export results
- No login required
- Free and unlimited use
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Frequently Asked Questions
What is effect size and why is it important?
Effect size measures the magnitude or practical significance of a relationship or difference, independent of sample size. While p-values tell whether an effect exists (statistical significance), effect sizes tell how large the effect is (practical significance). With large samples, even trivial differences can be statistically significant (p < .05). Effect sizes help interpret whether findings matter in real-world contexts. They enable meta-analysis by standardizing results across studies with different metrics. Report effect sizes with all statistical tests to give readers complete information about your findings' magnitude and importance.
How do I interpret Cohen's d effect size values?
Cohen's d conventions (differences between means in standard deviation units): d = 0.20 is small (subtle difference), d = 0.50 is medium (noticeable difference), d = 0.80 is large (substantial difference). However, these are rough guidelines - interpretation depends on context. In educational interventions, d = 0.20 may be meaningful if affecting thousands of students. In clinical psychology, d = 0.80 may be modest for intensive therapy. Consider practical significance in your field, costs of implementation, and comparison to existing interventions when interpreting effect sizes.
Should I report effect sizes even if my results are not statistically significant?
Yes! Effect sizes should be reported for all analyses, significant or not. Non-significant results with moderate effect sizes suggest inadequate statistical power (sample too small) rather than no effect. Non-significant results with small effect sizes indicate truly minimal effects. Reporting effect sizes for non-significant findings helps prevent publication bias and enables meta-analysts to include your data. It provides more complete information than p-values alone. Always report effect sizes with confidence intervals to show precision of estimates.