I thought the Wilcoxon signed rank test has similar assumptions to the Mann-Whitney U test... am I wrong?

I do not know why you should think so, The tests are different, require different measurement levels

of the dependent variablem, and are therefore designed for different problems.

But, since the U test is for ordinal data, normality considerations or homoscedascitiy considerations do

not apply (that can only be an issue in case of interval scaled dependent variables). Insofar you are

indeed right - both tests do not require normality or homoscedasxcity.

If so, the literature for the Mann-Whitney U test says that it is sensitive to departures for normality and homoscedacity if they happen simultaneously.

That does not make much sense. Mann-Whitney is for ranked data (ordinal data), so there is no normality or

nonormality, no hetero- or homoscedascity, and of course, there are no means Only if someone has the idea to use

that test for the comparison of means for interval scaled variables, this concepts could come into play. But I

do not know whether it makes any practical sense to use a rank based test for that purpose, since the data

would have to fulfil a dozen of usually unfulfilled distributional assumptions before test could make statements

about mean differences.

About the point you make with regards to generalizations with a small sample size n=5: I completely agree, and I do not plan on making any generalizations or bold claims. This is a pilot study, and the statistics will be mainly used for internal consumption.

So you could just work with the descriptive statistics.

However, I have faced the same problem with larger sample sizes (e.g. N = 30), and I have so far found no answers by searching the web, so I thought it would be a good opportunity to get an expert answer/opinion.

If you have problems with the distribution of the differences (not of the data, of course, since

not the data are analyses here but their within-subject differences), then you could use Wilcoxon

signed rank test. For t-test, problems with normality of the differences can be ignored if

sample size is large enough (n > 30 or som central limit theorem).

With kind regards

Karabiner