I have a large dataset containing the peak velocities of different people. I have then split this dataset into two depending on an attribute (for example male vs. female). Each dataset now contains approximately 30000 values. From this I have then created plotted the probability distributions of the two groups to see how they compare. This looks like the following:
I am quite new to statistics so am unsure how I could go about testing how different these two datasets are. Just by looking I would say there is some difference near the peak of both distributions and in the range 100-150 peak velocity. I want to know some statsitical method to show these differences I see are significant or not.
I originally thought of a student t-test, but I believe that is only for gaussian distributed data. From reading online, a two-sample Kolmogorov-Smirnov seems suitable to use as it is used to test whether two underlying one-dimensional probability distributions differ. However when I apply this to the datasets, I get a p-value that is basically 0. This seems a bit unlikely as the two datasets look very similar.
I hope I have given enough information, but if not please let me know. Thanks you.