Effect Size
Effect size quantifies the magnitude of a difference or relationship between variables, independent of sample size.
1Detailed Explanation
Effect size is crucial for understanding practical significance beyond statistical significance. Common measures include: Cohen's d for mean differences (small: 0.2, medium: 0.5, large: 0.8), relative risk (RR) and odds ratio (OR) for binary outcomes, correlation coefficient (r) for associations, and hazard ratio (HR) for time-to-event data. Standardized effect sizes allow comparison across studies. Effect sizes should be reported with CIs and interpreted in clinical context. Small p-values with negligible effect sizes are common in large samples and clinically meaningless. Reporting effect size is required by major reporting guidelines (CONSORT, STROBE, PRISMA).
2Examples
- A.Cohen's d = 0.6 indicates a medium effect — the treatment group scored half a standard deviation higher
- B.Odds ratio = 2.5 means the odds of the outcome are 2.5 times higher in the exposed group
3Why It Matters in Research
Effect sizes are essential for assessing practical significance and for meta-analysis. Overlooking them leads to misinterpretation of statistically significant but clinically trivial findings.
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