I am an old dog (DB guy) trying to learn new tricks (stats) and was hoping someone here could tell me if this is a good approach:
I have to analyse extremely right skewed counts of events over a period of observation, pre vs post. I have N=1213 pre and N=1138 post intervention datasets. Minitab rates Skewness at 5.3 and 7.3 for the two datasets. I would like to figure out the %change due to our intervention.
I proposed to do: transform non-zero event counts using log10 to make it less skewed and calculate the median change Mann-Whitney between pre and post. In addition, i calculate a probability of zero value events. In Minitab the Levene test for 2 sets of log10 values pre vs post gave P=0.220, so i assume that the variances are similar. With Mann-Whitney η1 = η2 vs η1 > η2 i had 0.4301 difference with P=0. So calculating after reversing the log gave me a 5.85 % improvement after intervention. The log curve wasn't considered normal(p<0.005).
But a teammate said i should be doing a bootstrap mean over the entire dataset including the zero event counts. So I did the Bootstrapping for mean in R, and this gave me over 50% improvement. This does not feel right.
What is the correct representation of the difference?