has part has_specified_input has_specified_output achieves_planned_objective statistical hypothesis test objective study design independent variable study design dependent variable data transformation false positive rate Wilcoxon signed rank test 1 2 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 paired observation comparison objective p-value ready for release