I've got a bunch of data from two samples (control and treated), each containing several thousand values which are to undergo significance testing in R. Theoretically, the values should be continuous, but due to rounding done by measurement software they aren't and they have got ties. The distributions are unknown and the shapes of control and treated distributions might be different, so I'd like to use a non-parametric test to compare if the difference across the samples is significant for 10 different factors.
I thought of using the Kolmogorov-Smirnov test, but it's not really suitable for ties. I recently stumbled upon a new R library called Matching that executes a bootstrap version of K-S test and tolerates ties. Now is this really a good idea or should I use another test instead? And do I need to adjust the p-value?