has part
has_specified_input
has_specified_output
achieves_planned_objective
p-value
statistical hypothesis test objective
study design independent variable
study design dependent variable
false positive rate
Wilcoxon signed rank test
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Orlaith Burke
Alejandra Gonzalez-Beltran
Philippe Rocca-Serra
The Wilcoxon signed rank test is a statistical test which tests the null hypothesis that the median difference between pairs of observations is zero. This is the non-parametric analogue to the paired t-test, and should be used if the distribution of differences between pairs may be non-normally distributed.
The procedure involves a ranking, hence the name. The absolute value of the differences between observations are ranked from smallest to largest, with the smallest difference getting a rank of 1, then next larger difference getting a rank of 2, etc. Ties are given average ranks. The ranks of all differences in one direction are summed, and the ranks of all differences in the other direction are summed. The smaller of these two sums is the test statistic, W (sometimes symbolized Ts). Unlike most test statistics, smaller values of W are less likely under the null hypothesis.
http://udel.edu/~mcdonald/statsignedrank.html
scipy.stats.wilcoxon(x, y=None, zero_method='wilcox', correction=False)
http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html#scipy.stats.wilcoxon
source:
https://github.com/scipy/scipy/blob/v0.15.1/scipy/stats/stats.py#L4103
signrank()
ranking
within subject comparison statistical test
continuous variable
categorical variable
within subject comparison objective
ready for release