Over enthusiastic MANOVA in R In recent research I pursue a MANOVA model on a dataset comprised of 50 measured characters and several grouping factors that I need to test. When I use something like this
summary(manova(malesM ~ popMales*manageMales*biomeMales), test = "Wilks") 

I  get 2.2e-16 significance for every possible output (also with other tests beside Wilks). The same thing I noticed when I use dummy, randomly generated factors. My data comes from a real-life study and I really can not belive that almost any theoretical, grouping factor will be significant. Any advice on how to refine my model, and to test this further? 
 A: This suggests to me that you may have one or a few observations that are extreme multivariate outliers for your dependent variables malesM, and that the MANOVA test is just picking up that the mean of any group containing such an outlier is far from the mean of those that don't. 
To check for this, you could look at the Mahalanobis distances of all the observations from the sample mean using the sample covariance matrix.
A: Second suggestion (MANOVA is not my strong point but no-one else has attempted to answer this question so I'll have another go):
50 measured characters may be too many $Y$ variables with a sample size of 853. The various tests all assume the $Y$ variables have a multivariate normal distribution and even then the $F$ tests are based on asymptotic approximations (for more than 2 $Y$ variables and more than 2 model d.f.). Though (at least some of) the tests are reasonably robust to departures from multivariate normality with reasonable sample sizes and a handful of $Y$ variables, I suspect that for many $Y$ variables the tests may become highly sensitive to departures from multivariate normality and/or the asymptotic approximations start to require really huge sample sizes.
Possibly someone else with more knowledge / relevant textbooks could confirm or deny this?
