(problem resolved; x,y and s1,s2 were of different size)
in R:
x <- c(373,398,245,272,238,241,134,410,158,125,198,252,577,272,208,260)
y <- c(411,471,320,364,311,390,163,424,228,144,246,371,680,384,279,303)
t.test(x,y)
t = -1.6229, df = 29.727, p-value = 0.1152
Same numbers are obtained in STATA and Excel
t.test(x,y,alternative="less")
t = -1.6229, df = 29.727, p-value = 0.05758
I cannot replicate the same result using either statsmodels.stats.weightstats.ttest_ind or scipy.stats.ttest_ind no matter which options I try.
statsmodels.stats.weightstats.ttest_ind(s1,s2,alternative="two-sided",usevar="unequal")
(-1.8912081781378358, 0.066740317997990656, 35.666557473974343)
scipy.stats.ttest_ind(s1,s2,equal_var=False)
(array(-1.8912081781378338), 0.066740317997990892)
scipy.stats.ttest_ind(s1,s2,equal_var=True)
(array(-1.8912081781378338), 0.066664507499812745)
There must be thousands of people who use Python to calculate t-test. Are we all getting incorrect results? (I typically rely on Python but this time I checked my results with STATA).