Effect Size Calculator
Cohen's d, Hedges' g, and what a 'large' effect looks like.
Work out Effect Size. Cohen's d, Hedges' g, and what a 'large' effect looks like. Shows the working, not just the answer.
Cohen's d
0.6667
Medium effect · Hedges' g = 0.658
Cohen's d expresses the difference between two means in standard deviations, which makes it comparable across studies measuring completely different things. The conventional bands — 0.2 small, 0.5 medium, 0.8 large — are Cohen's own rough guide and he was explicit that they were a last resort when no better benchmark exists in the field. Hedges' g applies a correction for small samples, where plain d is biased upward. Below about twenty per group the difference is worth reporting, and both are shown so the bias is visible rather than left to the reader to know about. The last two rows are the ones that make an effect size concrete. Overlap says how much the two distributions share; probability of superiority says how often a randomly picked member of group 1 exceeds a randomly picked member of group 2. A d of 0.8 — a "large" effect — still means the groups overlap by about 69%.
How the Effect Size Calculator works
Cohen's d with the small-sample correction, plus the two figures that make an effect size concrete: how much the distributions overlap, and how often a random member of one group exceeds a random member of the other.
Also known as: cohens d calculator · is this effect large · practical vs statistical significance · standardized mean difference
Frequently asked questions
What is Cohen's d?
The difference between two means expressed in pooled standard deviations. Because it is unitless it can be compared across studies measuring completely different things, which is why meta-analysis runs on it.
What counts as a large effect?
Cohen's rough guide is 0.2 small, 0.5 medium, 0.8 large — and he was explicit that these were a last resort when no field-specific benchmark exists. In some fields 0.2 is substantial and in others 0.8 is unremarkable.
When should I use Hedges' g instead?
Below about twenty per group. Cohen's d is biased upward in small samples and Hedges' g applies the correction. Both are shown here so the size of the bias is visible.
How much do groups with a large effect overlap?
More than people expect. A d of 0.8 — conventionally large — still leaves the two distributions overlapping by about 69%. That is why the overlap row is here.
Why report effect size at all if I have a p-value?
Because the p-value tells you whether a difference is detectable and the effect size tells you whether it matters. With a large enough sample any difference becomes significant, and only the effect size distinguishes an important one.
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