scipy tutorial - Baustatik-Info-Server

vs1 = 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_rel(a, b, axis=0)
Calculates the T-test on TWO RELATED samples of scores, a and b.
SciPy Reference Guide, Release 0.8.dev
This is a two-sided test for the null hypothesis that 2 related or repeated samples have identical average (expected)
values.
Notes
Parameters
a, b : sequence of ndarrays
The arrays must have the same shape.
axis : int, optional, (default axis=0)
Axis can equal None (ravel array first), or an integer (the axis over which to operate
on a and b).
Returns
t : float or array
t-statistic
prob : float or array
two-tailed p-value
Examples for the use are scores of the same set of student in different exams, or repeated sampling from the
same units. The test measures whether the average score differs significantly across samples (e.g. exams). If
we observe a large p-value, for example greater 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. Small p-values are associated with large t-statistics.
Examples
>>> from scipy import stats
>>> np.random.seed(12345678) # fix random seed to get same numbers
>>> rvs1 = stats.norm.rvs(loc=5,scale=10,size=500)
>>> rvs2 = (stats.norm.rvs(loc=5,scale=10,size=500) +
... stats.norm.rvs(scale=0.2,size=500))
>>> stats.ttest_rel(rvs1,rvs2)
(0.24101764965300962, 0.80964043445811562)
>>> rvs3 = (stats.norm.rvs(loc=8,scale=10,size=500) +
... stats.norm.rvs(scale=0.2,size=500))
>>> stats.ttest_rel(rvs1,rvs3)
(-3.9995108708727933, 7.3082402191726459e-005)
kstest(rvs, cdf, args=(), N=20, alternative=’two_sided’, mode=’approx’, **kwds)
Perform the Kolmogorov-Smirnov test for goodness of fit
3.18. Statistical functions (scipy.stats) 657