Cohen's d (Effect Size)
Worked example: means 105 vs 100 with sp 10 → d = 0.5 — press Try an example to run it live, then adjust anything.
Enter your known values, leave one input blank, and solves for the missing one. Tap a variable’s symbol to see what it means, with a typical value. Try different units for next level excitement!
Cohen's d (Effect Size) explained
A p-value tells you whether a difference is detectable; Cohen's d tells you whether it is big. Divide the gap between two means by the pooled standard deviation and you get the effect in standard-deviation units, comparable across studies and instruments. A treatment group averaging 105 against a control at 100 with sp = 10 gives d = 0.5. Jacob Cohen proposed the rough benchmarks of 0.2, 0.5 and 0.8 for small, medium and large in his 1969 power-analysis handbook — and spent much of the rest of his career warning people not to apply them mechanically, since a d of 0.1 on a mortality outcome can matter far more than a d of 1.0 on a lab task.
The trap this fixes is the significance illusion. With 10,000 subjects per arm, a d of 0.04 is highly significant and completely uninteresting; with 12 subjects, a d of 0.9 may fail to reach significance and still be the most important result in the paper. Report both. Reversed, the formula recovers scale: an intervention reported at d = 0.8 that moved a mean from 48 to 52 implies a pooled standard deviation of 4/0.8 = 5 in the original units.
Cohen's d (Effect Size) formula
- = Cohen's d
- = Mean of group 1
- = Mean of group 2
- = Pooled standard deviation
Missing one of these? Work it out first, then come back
- Cohen's d — Cohen's Kappa (Inter-Rater Agreement)
- Mean of group 1 — Weighted Mean of Two Groups, Z-Score (Standard Score)
- Mean of group 2 — Weighted Mean of Two Groups, Z-Score (Standard Score)
- Pooled standard deviation — Pooled Standard Deviation, Z-Score (Standard Score)