Effect Size
A quantitative measure of the magnitude of a phenomenon or the strength of a relationship between variables.
What is Effect Size?
Effect size quantifies the size of the difference between groups or the strength of an association, independent of sample size. While p-values tell you if an effect exists statistically, effect size tells you how large or meaningful that effect is in real-world terms. Common metrics include Cohen's d for standardized mean differences, Pearson's r for correlation, and odds ratios for categorical data.
Why Effect Size Matters
It is essential for interpreting the practical significance of findings, performing power analyses to determine necessary sample sizes, and conducting meta-analyses to synthesize results across multiple studies.
Example
A researcher testing a new math intervention finds a statistically significant improvement in scores (p < .01), but the effect size (Cohen's d = 0.1) indicates the actual increase is so small it may not be worth the cost of implementing the program.
Common Mistakes
- Reporting statistical significance (p-values) without reporting effect sizes.
- Interpreting small, medium, and large effect size benchmarks rigidly without considering the specific research context.