I then run a Wilcoxon rank sum test to compare, for each behaviour, the averages of durations, obtaining 12 p values, some of which are significant (values lower than alpha=0.05 ) The reviewer says that I need to correct alpha with Bonferroni, as I'm performing a multiple testing. This leads alpha to be very low: alpha corrected = .05/12 = 0.004.
the variables a Wilcoxon signed rank test was carried out. The mean of the positive ranks is larger than that for negative ranks suggesting that values for INT_DISE ASE are generally larger than for INT_UNIV . The Wilcoxon Signed rank test results in a Z statistic of -1.018 which results in an exact p value of .309. Use the Wilcoxon signed-rank test when there are two nominal variables measurement variable. One of the nominal variables has only two values, such as "before" and "after," and the other nominal variable often represents individuals. This is the non-parametric analogue to the paired -test, and you should use it if the distribution of

The Wilcoxon signed-rank test is a two-sided test that tests the null hypothesis that the median of the differences between the paired samples is equal to zero. If the test results in a p-value less than the significance level, we reject the null hypothesis, concluding that there is a significant difference between the paired samples.

The Wilcoxon signed rank sum test is the non-parametric version of a paired samples t-test. You use the Wilcoxon signed rank sum test when you do not wish to assume that the difference between the two variables is interval and normally distributed (but you do assume the difference is ordinal).

The Wilcoxon signed-rank test is a popular, nonparametric substitute for the t-test. It assumes that the data follow a symmetric distribution. The test is computed using the following steps. Subtract the hypothesized mean, absolute values. 0, from each data value. Rank the values according to their. . 277 74 450 94 229 286 411 331

what is wilcoxon signed rank test