text: what is the reliability and validity of the mann-whitney nonparametric test?   the test itself is mathematically sound.  what is dicey is how you interpret the relationships it finds.  basically, you always have a possibility that the data collected, just by chance, will cause the null hypothesis of no relationship to reject: that's the alpha thing.  you select the chances of a false finding by setting your p-value for rejection.  on the other hand, ignoring the chances of missing a relationship (beta), the real problem is understanding what any relationship found might mean.  this has nothing to do with the reliability or validity of the test; it has everything to do with the clarity and rigorousness of thought of the person using it.  a case in point, from my own history: ran one of those, and found an amazingly strong correlation between presence of one thing, and presense of another: doesn't matter what they were.  bottom line: the correlation reflected the fact that both things were results of something else, that i hadn't actually recognized as being a significant variable...  until i revisited the data, years later.  most embarassing.