How to Calculate Effect Size

Effect size measures how large a difference or relationship actually is — distinct from statistical significance, which only tells you whether an effect is likely to be real. A tiny, practically meaningless effect can still be "statistically significant" with a large enough sample.

Continuous outcomes

Mean difference (MD) — the raw difference between group means, useful when all studies use the same measurement scale.

Standardized mean difference (SMD) / Cohen's d — the mean difference divided by the pooled standard deviation, used when studies measure the same construct with different scales. Rough interpretation: 0.2 = small, 0.5 = medium, 0.8 = large — though this varies by field and should not be applied rigidly.

Hedges' g — a small-sample-corrected version of Cohen's d, generally preferable when individual study sample sizes are small.

Binary outcomes

Risk ratio (RR) — the ratio of event probability between two groups; intuitive to interpret (RR of 1.5 = 50% higher risk).

Odds ratio (OR) — the ratio of event odds between groups; common in case-control studies and logistic regression output, but less intuitive than RR and easily over-interpreted as if it were a risk ratio when event rates are high.

Hazard ratio (HR) — used in survival/time-to-event analysis, representing the instantaneous risk ratio over the follow-up period.

A common mistake

Mixing effect size types within the same meta-analysis (e.g., pooling odds ratios and risk ratios as if equivalent) without converting them to a common metric first. Most meta-analysis software includes conversion formulas — use them rather than approximating by eye.

Want the actual numbers instead of just the formulas? Our free effect size calculator computes Cohen's d, Hedges' g, odds ratio, and risk ratio (with 95% confidence intervals) instantly.