I would like some advice on how to implement a Kolmogorov-Smirnov test.
This is a condensed version of my previous question -- any further detail can be provided if needed.
I have data from n experimental trials. Each set consists of two paired time-series signals, for which I am calculating an informative "overlap" measure (a single summary value), based on some threshold value. I calculate this summary value for all n sets of data.
I would like to determine whether the distribution of these overlap values is non-random. The distribution of the data is not normal, and having read some details about different comparative tests of distributions, the Kolmogorov-Smirnov test seems most appropriate.
I'm hoping someone can give me advice on how to perform a permutation test using these data. My understanding is that this requires comparing the distribution of the actual data with those obtained from random combinations of the data, however, I'm uncertain of the procedure for implementing and interpreting the result. Any help would be much appreciated.