Hi all, I'm using R to analyze some research and I'm not sure which test would be appropriate for my data. I was hoping someone here might be able to help.
Short version: Evaluate null hypothesis that change A1->A2 is similar to change C1->C2, for continuous, non-normal datasets. Long version: I have two populations A and C. I take a measurement on samples of these populations before and after a process. So basically I have: A1 - sample of A before process A2 - sample of A after process C1 - sample of C (control) before process C2 - sample of C (control) after process The data is continuous and I have about 100 measurements in each dataset. Also, the data is not normally distributed (more like a Poisson). By Wilcoxon Rank Sum, A1 is significantly different than A2 and C1 is different than C2. Here is the problem: C1 is only slightly different than C2 (Wilcoxon, p<.02), while A1 is more noticeably different than A2 (p<1E-22). What I would like to do is assume that the changes seen in C are typical, and evaluate the changes in A relative to the changes in C (i.e. are the changes greater?). Any thoughts? Thanks, Peter Masny ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
