scipy tutorial - Baustatik-Info-Server

Notes
SciPy Reference Guide, Release 0.8.dev
We can use this test, if we observe two independent samples from the same or different population, e.g. exam
scores of boys and girls or of two ethnic groups. The test measures whether the average (expected) value differs
significantly across samples. If we observe a large p-value, for example larger than 0.05 or 0.1, then we cannot
reject the null hypothesis of identical average scores. If the p-value is smaller than the threshold, e.g. 1%, 5%
or 10%, then we reject the null hypothesis of equal averages.
Examples
>>> from scipy import stats
>>> import numpy as np
>>> #fix seed to get the same result
>>> np.random.seed(12345678)
test with sample with identical means
>>> rvs1 = stats.norm.rvs(loc=5,scale=10,size=500)
>>> rvs2 = stats.norm.rvs(loc=5,scale=10,size=500)
>>> stats.ttest_ind(rvs1,rvs2)
(0.26833823296239279, 0.78849443369564765)
test with sample with different means
>>> rvs3 = stats.norm.rvs(loc=8,scale=10,size=500)
>>> stats.ttest_ind(rvs1,rvs3)
(-5.0434013458585092, 5.4302979468623391e-007)
ttest_onesamp(a, popmean)
Calculates the T-test for the mean of ONE group of scores a.
This is a two-sided test for the null hypothesis that the expected value (mean) of a sample of independent
observations is equal to the given population mean, popmean.
Parameters
a : array_like
sample observation
popmean : float or array_like
expected value in null hypothesis, if array_like than it must have the same shape as
a excluding the axis dimension
axis : int, optional, (default axis=0)
Axis can equal None (ravel array first), or an integer (the axis over which to operate
on a).
Returns
t : float or array
t-statistic
prob : float or array
two-tailed p-value
3.18. Statistical functions (scipy.stats) 693