Cohen's h (Effect Size for Proportions)
Computes Cohen's h, the effect-size measure for the difference between two proportions. Comparing proportions directly is misleading, because the same arithmetic difference weighs differently near the middle (0.5) and near the ends (0 or 1). Cohen's h fixes this by applying the arcsine transformation, which stabilizes the variance, before measuring the distance. Usual convention: 0.2 is small, 0.5 medium and 0.8 large. Enter the two proportions (between 0 and 1).
Resultado
—
Cohen's h (Effect Size for Proportions)
Computes Cohen's h, the effect-size measure for the difference between two proportions. Comparing proportions directly is misleading, because the same arithmetic difference weighs differently near the middle (0.5) and near the ends (0 or 1). Cohen's h fixes this by applying the arcsine transformation, which stabilizes the variance, before measuring the distance. Usual convention: 0.2 is small, 0.5 medium and 0.8 large. Enter the two proportions (between 0 and 1).
The fair effect size between two proportions
Saying one proportion rose from 0.50 to 0.55 and another from 0.90 to 0.95 looks like the same five-point improvement. But statistically it isn't: gaining five points starting from 90% is much harder and more significant than starting from 50%. Cohen's h corrects this distortion, giving an effect size that weighs proportions fairly.
The secret lies in the arcsine transformation applied to the square root of each proportion. That transformation stretches the regions near 0 and 1 and compresses the middle, so that equal differences on the transformed scale represent comparable difficulties. Cohen's h is simply the distance between the two already-transformed proportions, and therefore doesn't suffer from the scale problem of the raw difference.
Enter the two proportions, each between 0 and 1. The tool returns the value of h. To interpret it, Cohen's convention suggests 0.2 as a small effect, 0.5 as medium and 0.8 as large. It's the measure of choice for planning the sample size of studies comparing proportions and for reporting the magnitude of a difference beyond the p-value.
Related Tools
Eta-Squared (Effect Size)
Computes eta-squared (η²), an effect-size measure for analysis of variance, from the F-statistic and the degrees of freedom. While the F-test says whether there's a significant difference between groups, η² says how much of that difference the grouping variable explains — the proportion of total variance attributable to the groups. It's essential for reporting ANOVA results beyond the p-value. Enter the F-statistic and the numerator and denominator degrees of freedom.
Common-Language Effect Size (CLES)
Computes the common-language effect size (CLES), also called the probability of superiority. It's the most intuitive way to communicate a difference between groups: the probability that a value drawn from the first group is larger than a value drawn from the second. A CLES of 0.70 means that, in 70% of random comparisons, the first group wins. Ties count as half. It's the friendly version of Cliff's delta. Enter the two groups of values.
Cliff's Delta (Effect Size)
Computes Cliff's delta, a non-parametric effect-size measure between two groups. It answers a direct question: if I draw a value at random from each group, how much more likely is one to be larger than the other? It ranges from −1 to +1; zero means complete overlap. Because it works only with order comparisons, it requires no assumptions about the distribution and is robust to outliers. It's the ideal companion to the Mann-Whitney test. Enter the two groups of values.
The results provided by this tool are for general informational and educational purposes only and do not constitute professional, financial, medical, legal, tax or accounting advice. Always confirm important decisions with a qualified professional and official sources.