# Effect Size Calculator (Cohen's d)

Free effect size calculator. Calculate Cohen's d and Hedges' g to measure the practical significance of differences between two groups.

## What this calculates

Calculate Cohen's d and Hedges' g effect sizes to measure the practical significance of the difference between two group means. While p-values tell you if a difference exists, effect size tells you how large it is.

## Inputs

- **Group 1 Mean** — Mean of the first group (e.g., treatment group).
- **Group 2 Mean** — Mean of the second group (e.g., control group).
- **Group 1 Std Dev** — min 0.0001 — Standard deviation of the first group.
- **Group 2 Std Dev** — min 0.0001 — Standard deviation of the second group.
- **Group 1 Size** — min 2 — Number of observations in group 1.
- **Group 2 Size** — min 2 — Number of observations in group 2.

## Outputs

- **Cohen's d** — The standardized mean difference.
- **Interpretation** — formatted as text — Cohen's benchmarks for effect size magnitude.
- **Pooled Standard Deviation** — The pooled standard deviation used in the calculation.
- **Hedges' g (bias-corrected)** — Cohen's d with small-sample bias correction.
- **Overlap (%)** — Approximate percentage of overlap between the two distributions.

## Details

Effect size quantifies the magnitude of the difference between two groups.

Cohen's d:
d = (M₁ - M₂) / s_pooled

Where s_pooled = √[((n₁-1)s₁² + (n₂-1)s₂²) / (n₁+n₂-2)]

Cohen's Benchmarks

- Small: d = 0.2

- Medium: d = 0.5

- Large: d = 0.8

Hedges' g applies a correction for small sample bias.

Why effect size matters: A study with a large sample may find a statistically significant but practically trivial difference. Effect size tells you whether the difference matters in practice.

## Frequently Asked Questions

**Q: What is the difference between Cohen's d and Hedges' g?**

A: Cohen's d and Hedges' g both measure standardized mean differences. Hedges' g includes a correction factor that removes the small upward bias in Cohen's d for small samples. For large samples (n > 20 per group), the values are nearly identical. Use Hedges' g for small samples.

**Q: Why is effect size important alongside p-values?**

A: P-values depend heavily on sample size. With a large enough sample, even a tiny, meaningless difference becomes statistically significant. Effect size is independent of sample size and tells you the practical magnitude of the difference. Always report both.

**Q: What does the overlap percentage mean?**

A: Overlap represents how much the two group distributions overlap. 100% overlap means the groups are identical. A Cohen's d of 0.8 (large effect) corresponds to about 69% overlap. Even with large effects, there is substantial overlap between groups.

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Source: https://vastcalc.com/calculators/statistics/effect-size
Category: Statistics
Last updated: 2026-04-21