Effect Size Calculator

Calculate a standardised effect size between two group means.

About Effect Size Calculator

Effect size expresses the difference in standard deviations, making it comparable across studies and independent of sample size. Required sample size scales with the inverse square of the effect size. Statistical significance depends on sample size while effect size does not, which is why large studies can find significant but trivial differences.

Formula

Effect Size

Cohen’s d = (mean₁ − mean₂) ÷ pooled standard deviation

Cohen’s d: Difference expressed in standard deviations, so it is unit free
conventions: 0.2 small, 0.5 medium, 0.8 large

Worked example

With the default values below, Effect Size Calculator returns Cohen’s d of 0.50000. Change any input above and every figure here is recalculated in your browser.

Inputs used

Mean of group 1: 52
Mean of group 2: 47
Pooled standard deviationminimum 0: 10
Sample size per groupminimum 2: 60

Results

Cohen’s d: 0.50000
Magnitude: Medium
Distribution Overlap: 81.00%
Raw Mean Difference: 5.0000
Sample Size per Group for 80 Per Cent Power: 63

Step by step

1Values used
2Effect Size
3Cohen’s d= 0.50000
4Magnitude= Medium
5Distribution Overlap= 81.00
6Raw Mean Difference= 5.0000
7Sample Size per Group for 80 Per Cent Power= 63

Assumptions and limits

The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 5 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

How is Effect Size calculated?

Cohen’s d = (mean₁ − mean₂) ÷ pooled standard deviation Effect size expresses the difference in standard deviations, making it comparable across studies and independent of sample size. Required sample size scales with the inverse square of the effect size.

Why does Effect Size matter?

Statistical significance depends on sample size while effect size does not, which is why large studies can find significant but trivial differences.

What values do I need to enter?

This calculator takes 4 inputs: Mean of group 1, Mean of group 2, Pooled standard deviation, Sample size per group. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.