scipy.stats.wilcoxon¶
- scipy.stats.wilcoxon(x, y=None, zero_method='wilcox', correction=False)[source]¶
Calculate the Wilcoxon signed-rank test.
The Wilcoxon signed-rank test tests the null hypothesis that two related paired samples come from the same distribution. In particular, it tests whether the distribution of the differences x - y is symmetric about zero. It is a non-parametric version of the paired T-test.
Parameters: x : array_like
The first set of measurements.
y : array_like, optional
The second set of measurements. If y is not given, then the x array is considered to be the differences between the two sets of measurements.
zero_method : string, {“pratt”, “wilcox”, “zsplit”}, optional
- “pratt”:
Pratt treatment: includes zero-differences in the ranking process (more conservative)
- “wilcox”:
Wilcox treatment: discards all zero-differences
- “zsplit”:
Zero rank split: just like Pratt, but spliting the zero rank between positive and negative ones
correction : bool, optional
If True, apply continuity correction by adjusting the Wilcoxon rank statistic by 0.5 towards the mean value when computing the z-statistic. Default is False.
Returns: T : float
The sum of the ranks of the differences above or below zero, whichever is smaller.
p-value : float
The two-sided p-value for the test.
Notes
Because the normal approximation is used for the calculations, the samples used should be large. A typical rule is to require that n > 20.
References
[R318] http://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test