Effect Size Calculator (Cohen's d / η² / converter)
P-values say whether a difference exists; effect sizes say how large — a required metric for papers and meta-analysis. This tool: two group means → Cohen's d and Hedges's g (with 95% CI); ANOVA F → η²/partial η²; plus d↔r↔OR conversion. Computed locally; data never uploaded.
① Input
| Mean | SD | n | |
|---|---|---|---|
| Group 1 | |||
| Group 2 |
How to use & methodology
Why report an effect size?
P-values depend on sample size — with a large sample even a tiny difference can be significant. An effect size (e.g. Cohen's d) is independent of sample size and quantifies the magnitude directly; it is required by CONSORT, meta-analysis and most journals.
Cohen's d vs Hedges's g?
g multiplies d by a small-sample correction factor that fixes d's overestimation at small samples. Prefer g when n is small; at large n the two are nearly identical.
η² or partial η²?
Partial η² is the variance an effect explains relative to 'that effect + error', more commonly reported in factorial ANOVA; this tool gives partial η² directly from F and the degrees of freedom.
Are the d↔r↔OR conversions reliable?
They are common approximate conversions used in meta-analysis to harmonize effect sizes from different designs. They assume continuous variables are approximately normal and binary outcomes arise from a latent continuous variable; deviations introduce error, so interpret with care.