# 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
xarray_like

The first set of measurements.

yarray_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_methodstring, {“pratt”, “wilcox”, “zsplit”}, optional
“pratt”:

Pratt treatment: includes zero-differences in the ranking process (more conservative)

“wilcox”:

“zsplit”:

Zero rank split: just like Pratt, but splitting the zero rank between positive and negative ones

correctionbool, 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
statisticfloat

The sum of the ranks of the differences above or below zero, whichever is smaller.

pvaluefloat

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

1

https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test

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