I'm responsible for a charity donation site. We're about the change the site design, and we want to know the best way of detecting if the distribution of donations changes after the design. The problem is the data is quite clumpy, particularly around \$5, \$10, \$20, \$25 and \$50 values, with \$15 being relatively rare. There are nonetheless other real values, particularly between \$20 and \$100. 


The consequence is that the stdev is three times the mean, so my first approach, a T-Test with some correction for the skew, doesn't seem feasible. If our re-design only has a moderate impact it's unlikely a T-test will be able to detect it with certainty. 


Binning into \$5 bins and running a G-Test individually means I can make statements about individual categories, but I'm worried about drawing overall conclusions from such a series of measurements. I've read briefly about using Fisher's method for combining p-values but I'm not sure how to explain the result, particularly as a positive result could mean *fewer* \$10 donations but more \$15 donations.


I'm certain that clumpy data is a known phenomenon, but Googling hasn't helped, and my background is CS, not stats. Would anyone know the best way to handle this?



Detecting a shift in distribution where the distribution is clumpy
The [Kolmogorv-Smirnov](http://en.wikipedia.org/wiki/Kolmogorov%25E2%2580%2593Smirnov_test) test suggested by Steve Huntsman seems to have done the job.


