Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up.
Sign up to join this community
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
$d = \frac{\bar{x}_1 - \bar{x}_2}{s}$
with
$s = \sqrt{\frac{(n_1-1)s^2_1 + (n_2-1)s^2_2}{n_1+n_2 - 2}}$
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.
