I am doing a Wilcoxon rank sum test (aka Wilcoxon-Mann-Whitney) with R. There are two variables "ScorePerView", that is metric and "HasCodeElement", that is binary.
So the syntax is like that
wilcox.test(ScorePerView ~ HasCodeElement, data=Datenmatrix, paired=FALSE, alternative='less')
When the result is significant (p-value < 0.005), then one distribution is lower or equal to the other.
But which one is lower?
Is the one where HasCodeElement is 0 is lower (or equal) then the one where HasCodeElement is 1.
Or is it the other way where HasCodeElement is 1 is lower (or equal) then the one where HasCodeElement is 0.
How can I interpret the R output on an one tailed test?
Here is my result with the syntax above.
Found the answer here:
How do I interpret the Mann-Whitney U when using R's formula interface
Also marked the Question as duplicated.
I don't think you should use the formula interface with
~. If you look at the syntax of the function
wilcox.test here you will see that you can also separate your two samples as
y and use
wilcox.test(x,y,...). This will remove any ambiguity. If you use
wilcox.test(x,y,paired=FALSE,alternative="less"), and the p-value is significant, then it means that
y come from different populations, and that
y comes from a population with larger values than