What is Effect size?
Effect size is a quantitative measure of the magnitude of a phenomenon, independent of sample size. While statistical significance tells you whether an effect exists, effect size tells you how big it is and whether it matters practically.
How it works
Common effect size measures include Cohen’s d (difference between groups in standard deviation units), correlation coefficients (r), odds ratios, and R-squared. Cohen’s benchmarks (d=0.2 small, 0.5 medium, 0.8 large) provide rough guides, but practical significance depends on context: a small effect on a population-level intervention can have enormous aggregate impact. The replication crisis revealed that many published studies reported statistically significant results with effect sizes too small to be meaningful.
Applied example
A new educational intervention produces a statistically significant improvement (p
Why it matters
Effect size is the essential complement to statistical significance, answering the question that actually matters for practice: not ‘Is the effect real?’ but ‘Is the effect large enough to care about?’
