Effect Size Calculator (Cohen’s d)
Here’s a concise and informative table summarizing key points about effect size in relation to the null hypothesis:
| Aspect | Explanation |
|---|---|
| Definition | Effect size measures the magnitude of difference or association between groups, independent of sample size. |
| Role in Null Hypothesis | Determines practical significance beyond statistical significance (p-value). |
| Null Hypothesis (H₀) | Assumes effect size is zero (no meaningful effect). |
| Alternative Hypothesis (H₁) | Suggests a non-zero effect size (meaningful effect). |
| Common Measures | Cohen’s d, Pearson’s r, Odds Ratio (OR), Eta squared (η²), Omega squared (ω²) |
| Interpretation (Cohen’s d) | Small: 0.2, Medium: 0.5, Large: 0.8 |
| Interpretation (Pearson’s r) | Small: 0.1, Medium: 0.3, Large: 0.5 |
| Importance | Helps distinguish statistically significant results from practically meaningful findings. |
| Relationship to Sample Size | Effect size is independent of sample size (unlike p-value). |
| Confidence Intervals | Reported with effect sizes to convey precision of estimation. |
This table provides a quick reference for understanding the concept and relevance of effect sizes when interpreting hypothesis testing.