How do I calculate the effect-size of an unequal sample size independent t test? Given the following parameters, how do I calculate the effect size of an independent unequal sample size t test?
sample 1:

size = 32 
mean = 845.1 
std  = 46.7

sample 2:
size = 53
mean = 829.1
std  = 33.8

test results:
tstat   = 1.83
p value = .035
df      = 83

 A: Here is the python script to calculate the values you are interested in. Following code is also available here - https://gist.github.com/sriisking/10716f107ed30f4911639d695e3fbe49
from scipy.stats import ttest_ind_from_stats
import numpy as np

#We use Welch's t-Test as the sample sizes are different
def statistical_significance_welch_ttest(mean1,std1,count1,mean2,std2,count2):
    t_statistic, p_value = ttest_ind_from_stats(mean1, std1, count1,
                                            mean2, std2, count2,
                                            equal_var=False)
    return t_statistic, p_value

#Here the pooled standard deviation accounts for unequal sample sizes
def effect_size_cohensD(mean1,std1,count1,mean2,std2,count2):
    dof = (count1 + count2 - 2)
    cohens_d = (mean1 - mean2) / np.sqrt(((count1 - 1) * std1 ** 2 + (count2 - 1) * std2 ** 2) / dof)
    return cohens_d

if __name__ == '__main__':
    mean1 = 845.1; std1 = 46.7;count1 = 32
    mean2 =829.1; std2 =33.8; count2 = 53
    t_statistic, p_value = statistical_significance_welch_ttest(mean1,std1,count1,mean2,std2,count2)
    print t_statistic, p_value
    cohens_d = effect_size_cohensD(mean1, std1, count1, mean2, std2, count2)
    print cohens_d

A: You can calculate:


*

*Cohen's $d=\dfrac{\bar{x}_1-\bar{x}_2}{s_p}$, where $s_p=\sqrt{\dfrac{(n_1-1)s_1^2+(n_2-1)s_2^2}{n_1+n_2-2}}$

*Hedges' $g=d \times \dfrac{\Gamma(\frac{m}{2})}{\Gamma(\frac{m-1}{2})\sqrt{\frac{m}{2}}}$, where $m=n_1+n_2-2$.

*Glass' $\Delta = \dfrac{\bar{x}_1-\bar{x}_2}{s_c}$, where $s_c = s_1 \text{ or } s_2$, depending on which is the "control" group.
See: Hedges, L. V. 1981. Distribution theory for Glass’s estimator of effect size and related estimators. Journal of Educational Statistics 6: 107–128. 

EDIT: PS. None are dependent on equal sample sizes.
