t-test and wilcoxon-test examples in Python

statistical_tests.py

# Mathieu Blondel, February 2012
# License: BSD 3 clause

# Port to Python of examples in chapter 5 of
# "Introductory Statistics with R" by Peter Dalgaard

import numpy as np
from scipy.stats import ttest_1samp, wilcoxon, ttest_ind, mannwhitneyu

# daily intake of energy in kJ for 11 women
daily_intake = np.array([5260,5470,5640,6180,6390,6515,
                         6805,7515,7515,8230,8770])

# one sample t-test
# null hypothesis: expected value = 7725
t_statistic, p_value = ttest_1samp(daily_intake, 7725)

# p_value < 0.05 => alternative hypothesis:
# data deviate significantly from the hypothesis that the mean
# is 7725 at the 5% level of significance
print "one-sample t-test", p_value

# one sample wilcoxon-test
z_statistic, p_value = wilcoxon(daily_intake - 7725)
print "one-sample wilcoxon-test", p_value

energ = np.array([
# energy expenditure in mJ and stature (0=obese, 1=lean)
[9.21, 0],
[7.53, 1],
[7.48, 1],
[8.08, 1],
[8.09, 1],
[10.15, 1],
[8.40, 1],
[10.88, 1],
[6.13, 1],
[7.90, 1],
[11.51, 0],
[12.79, 0],
[7.05, 1],
[11.85, 0],
[9.97, 0],
[7.48, 1],
[8.79, 0],
[9.69, 0],
[9.68, 0],
[7.58, 1],
[9.19, 0],
[8.11, 1]])

# similar to expend ~ stature in R
group1 = energ[:, 1] == 0
group1 = energ[group1][:, 0]
group2 = energ[:, 1] == 1
group2 = energ[group2][:, 0]

# two-sample t-test
# null hypothesis: the two groups have the same mean
# this test assumes the two groups have the same variance...
# (can be checked with tests for equal variance)
# independent groups: e.g., how boys and girls fare at an exam
# dependent groups: e.g., how the same class fare at 2 different exams
t_statistic, p_value = ttest_ind(group1, group2)

# p_value < 0.05 => alternative hypothesis:
# they don't have the same mean at the 5% significance level
print "two-sample t-test", p_value

# two-sample wilcoxon test
# a.k.a Mann Whitney U
u, p_value = mannwhitneyu(group1, group2)
print "two-sample wilcoxon-test", p_value

# pre and post-menstrual energy intake
intake = np.array([
[5260, 3910],
[5470, 4220],
[5640, 3885],
[6180, 5160],
[6390, 5645],
[6515, 4680],
[6805, 5265],
[7515, 5975],
[7515, 6790],
[8230, 6900],
[8770, 7335],
])

pre = intake[:, 0]
post = intake[:, 1]

# paired t-test: doing two measurments on the same experimental unit
# e.g., before and after a treatment
t_statistic, p_value = ttest_1samp(post - pre, 0)

# p < 0.05 => alternative hypothesis:
# the difference in mean is not equal to 0
print "paired t-test", p_value

# alternative to paired t-test when data has an ordinary scale or when not
# normally distributed
z_statistic, p_value = wilcoxon(post - pre)

print "paired wilcoxon-test", p_value

Great :D Thank you!

This is helpful, thanks for putting all the tests in one place.

This is helpful. Thx a lot.

Thanks, this is very useful, please can you also p.adust (like FDR, Benjamini-Hochberg, bonf method)? It would be great. Thanks again