I have data from 100 individuals doing a 2AFC under a control and a manipulated setting. I want to compare the means. I initially used a paired t-test. My supervisor noticed the distributions were not normal and recommended me using the Mann–Whitney U test instead of the t-test. However, as far as I can tell, the Mann–Whitney U test does not allow for paired data. Or does it? What non-parametric test should I use? What test generalizes the paired t-test? In other words, I have a non-normal distribution and I want to test how different its mean is from zero.


The non-parametric analog of the paired $t$-test is the Wilcoxon.

  • $\begingroup$ My data are "True"/"False" answers, averaged for both control and experimental over 36 trials. Each individual, therefore, has two numbers between 0 and 1 indicating the 'proportion correct'. $\endgroup$ – LBogaardt Aug 24 '14 at 15:57
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    $\begingroup$ In that case, the Wilcoxon is not the ideal test to use. For each participant you have 36 or 72 data, ie >2. When you reduce your data to 2 points by averaging, you are throwing data away. You would have better power by using a GLMM or GEE logistic regression (my answer here: Difference between generalized linear models & generalized linear mixed models in SPSS discusses the difference). $\endgroup$ – gung - Reinstate Monica Aug 24 '14 at 16:20
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    $\begingroup$ +1 but I'd usually avoid referring to the signed rank test simply as "the Wilcoxon" without further adornment, since there's more than one test associated with the name Wilcoxon. $\endgroup$ – Glen_b Aug 25 '14 at 1:34
  • $\begingroup$ That's a reasonable point, @Glen_b. In my mind there is the Mann-Whitney U-test & the Wilcoxon test, but I should probably be clearer since many don't use the names that way (frustratingly, R has both listed under wilcox.test()). $\endgroup$ – gung - Reinstate Monica Aug 25 '14 at 2:18
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    $\begingroup$ Well, Wilcoxon deserves credit somewhere for the Mann-Whitney, since the rank sum test is his invention and that and the U-test are equivalent. Mann and Whitney's contribution is substantial (Wilcoxon's tables were very limited for starters, to the point of being not very useful, and the whole U-statistics thing is a big deal), but Wilcoxon certainly has priority. $\endgroup$ – Glen_b Aug 25 '14 at 2:22

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