Effect Size (Cohen's d) Calculator
Effect Size (Cohen's d) Calculator
Enter the mean, standard deviation, and sample size for each of your two groups. This calculates Cohen's d, an effect size measure that expresses the difference between the groups' means in standard deviation units — letting you judge practical importance separately from statistical significance.
- Formula: d = (mean1 − mean2) ÷ pooled standard deviation, where the pooled SD combines both groups' variability weighted by their sample sizes.
- Complements significance testing, doesn't replace it: a t-test tells you whether a difference is statistically significant, while Cohen's d tells you how big that difference actually is — a large sample can make a tiny, practically meaningless difference statistically significant.
- Standard interpretation thresholds (Cohen's original convention): |d| around 0.2 is a small effect, around 0.5 is medium, and 0.8 or above is large — though what counts as practically meaningful still depends on your specific field.
Why does effect size matter alongside a p-value?
A p-value only tells you whether an observed difference is unlikely to be due to chance, not whether that difference is large enough to matter in practice — Cohen's d fills that gap by quantifying the actual size of the difference.
Can Cohen's d be negative?
Yes — the sign simply reflects which group has the higher mean (positive means group 1 is higher, negative means group 2 is higher); the magnitude (absolute value) is what determines the effect size category.
Effect Size (Cohen's d) Calculator
Enter the mean, standard deviation, and sample size for each of your two groups. This calculates Cohen's d, an effect size measure that expresses the difference between the groups' means in standard deviation units — letting you judge practical importance separately from statistical significance.
- Formula: d = (mean1 − mean2) ÷ pooled standard deviation, where the pooled SD combines both groups' variability weighted by their sample sizes.
- Complements significance testing, doesn't replace it: a t-test tells you whether a difference is statistically significant, while Cohen's d tells you how big that difference actually is — a large sample can make a tiny, practically meaningless difference statistically significant.
- Standard interpretation thresholds (Cohen's original convention): |d| around 0.2 is a small effect, around 0.5 is medium, and 0.8 or above is large — though what counts as practically meaningful still depends on your specific field.
Why does effect size matter alongside a p-value?
A p-value only tells you whether an observed difference is unlikely to be due to chance, not whether that difference is large enough to matter in practice — Cohen's d fills that gap by quantifying the actual size of the difference.
Can Cohen's d be negative?
Yes — the sign simply reflects which group has the higher mean (positive means group 1 is higher, negative means group 2 is higher); the magnitude (absolute value) is what determines the effect size category.