ttest_cohensd.py

"""
Here is the python script to calculate the values you are interested in.
"""
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):
    cohens_d = (mean1 - mean2) / np.sqrt(((count1 - 1) * std1 ** 2 + (count2 - 1) * std2 ** 2) / (count1 + count2 - 2))
    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