Use this calculator to compute Cohen's d for two independent groups from means, standard deviations, and sample sizes. It also reports an interpretation label and optional Hedges' g correction for smaller samples.
What Is Cohen's d?
Cohen's d is a standardized effect size that tells you how far apart two group means are in standard deviation units. Instead of only saying "the means are different," Cohen's d helps quantify how much they differ in a scale-free way. This makes comparisons across studies easier.
Formula Used in This Calculator
For two independent groups, the calculator uses:
d = (M₁ - M₂) / SDpooled
Where pooled standard deviation is:
SDpooled = √[ ((n₁ - 1)SD₁² + (n₂ - 1)SD₂²) / (n₁ + n₂ - 2) ]
If selected, it also computes Hedges' g: g = J × d, where J = 1 - 3 / (4(n₁+n₂) - 9). Hedges' g slightly shrinks d to reduce upward bias in small samples.
How to Interpret d
Common rough benchmarks (always context-dependent):
- < 0.20: negligible effect
- 0.20–0.49: small effect
- 0.50–0.79: medium effect
- 0.80–1.19: large effect
- 1.20+: very large effect
The sign of d indicates direction. A positive d means Group 1's mean is higher than Group 2's mean; a negative d means the opposite.
When to Use This Calculator
- Comparing two independent groups (e.g., treatment vs control).
- Summarizing practical significance beyond p-values.
- Preparing inputs for meta-analysis or power planning.
When Not to Use It
- Paired/repeated-measures data (use paired-samples effect sizes).
- Highly non-normal or heavily skewed distributions without caution.
- Very unequal variances where alternative standardization may be preferred.
Reporting Example
"Participants in the intervention group scored higher (M = 78.4, SD = 10.2, n = 35) than the control group (M = 72.1, SD = 9.8, n = 32), corresponding to a medium effect, d = 0.63 (Hedges' g = 0.62)."
Practical Notes
- Effect size should be interpreted with subject-matter expertise, not rules alone.
- Large d values can still be uncertain with small sample sizes.
- Use confidence intervals when possible for a fuller statistical picture.