I've been redirected here from another forum...
I have used a two sample Kolmogorov-Smirnov test to compare the distributions of two sets of data. Basically I am comparing the error distributions between two measurements when an intervention is made, to determine whether the intervention (a changing measurement parameter) significantly changes the error distribution of the measurement.
I know that the K-S test is a non parametric test, however the distributions of data I'm comparing has turned out to be normally distributed... I know there is probably a number of tests that could be used to compare normally distributed data, but is there a reason not to use the K-S test? Are there any disadvantages (with regard to type1 and 2 errors perhaps)? Is it ok to use it?
I've sort of gone down this route with my data analysis, but the question has come up: why use a non parametric test to compare parametric data? Hopefully K-S is unconventional rather than completely wrong.