I am conducting statistical tests on a large set of frequency data. The frequency data is in the form of times of occurrence of events at a number of locations. Locations can be assumed to be identical. I want to test whether a certain subset of these results differs significantly from the rest of the distribution. The subset is defined with a known set of locations/time spans.
I have considered using average time intervals between events with something like a Kolmogorov–Smirnov test or a Mann–Whitney U test. However, I am out of my depth and am unsure of the appropriate way to analyze my data. Any suggestions?