python - Perform 2 sample t-test -


i have mean, std dev , n of sample 1 , sample 2 - samples taken sample population, measured different labs.

n different sample 1 , sample 2. want weighted (take n account) two-tailed t-test.

i tried using scipy.stat module creating numbers np.random.normal, since takes data , not stat values mean , std dev (is there way use these values directly). didn't work since data arrays has of equal size.

any on how p-value highly appreciated.

if have original data arrays a , b, can use scipy.stats.ttest_ind argument equal_var=false:

t, p = ttest_ind(a, b, equal_var=false)

if have summary statistics of 2 data sets, can calculate t value using scipy.stats.ttest_ind_from_stats (added scipy in version 0.16) or formula (http://en.wikipedia.org/wiki/welch%27s_t_test).

the following script shows possibilities.

from __future__ import print_function  import numpy np scipy.stats import ttest_ind, ttest_ind_from_stats scipy.special import stdtr  np.random.seed(1)  # create sample data. = np.random.randn(40) b = 4*np.random.randn(50)  # use scipy.stats.ttest_ind. t, p = ttest_ind(a, b, equal_var=false) print("ttest_ind:            t = %g  p = %g" % (t, p))  # compute descriptive statistics of , b. abar = a.mean() avar = a.var(ddof=1) na = a.size adof = na - 1  bbar = b.mean() bvar = b.var(ddof=1) nb = b.size bdof = nb - 1  # use scipy.stats.ttest_ind_from_stats. t2, p2 = ttest_ind_from_stats(abar, np.sqrt(avar), na,                               bbar, np.sqrt(bvar), nb,                               equal_var=false) print("ttest_ind_from_stats: t = %g  p = %g" % (t2, p2))  # use formulas directly. tf = (abar - bbar) / np.sqrt(avar/na + bvar/nb) dof = (avar/na + bvar/nb)**2 / (avar**2/(na**2*adof) + bvar**2/(nb**2*bdof)) pf = 2*stdtr(dof, -np.abs(tf))  print("formula:              t = %g  p = %g" % (tf, pf))

the output:

ttest_ind:            t = -1.5827  p = 0.118873 ttest_ind_from_stats: t = -1.5827  p = 0.118873 formula:              t = -1.5827  p = 0.118873

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