I know my two samples have unequal variance. Should I consider this in my calculation of the effect size? I'm using Cohen's d, which Wikipedia defines as

$d = \frac{\bar{x}_1 - \bar{x}_2}{s}$


$s = \sqrt{\frac{(n_1-1)s^2_1 + (n_2-1)s^2_2}{n_1+n_2 - 2}}$

However, I noticed that when performing the t-test assuming unequal variance, a different estimation of the variance is used (instead of the pooled variance). Should I adapt the Cohen's d formula as well?


When doing t tests, we divide the estimated mean difference with standard error \sqrt{Var}/\sqrt{N}. Whereas in Cohen's d, we divide the mean difference with pooled standard deviation. They are usually different.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.