I have run 10,000 random samples (910 data points each) on a data set of about 75,000 data points. I would like to make a continuous distribution out of this so that I can test the probability of getting the results of a particular non-random sample which I made based on theoretical concerns.
For each random sample (and for the "real" sample), I collected the number of hits, the number of hits + misses (this number varies somewhat for reasons which I don't think are important), and the relative frequency of hits (hits / hits+misses).
Ideally, I'd like to take the relative frequencies and turn it into a continuous distribution (I assume it will be roughly normal), so that I can then see how likely the "real" relative frequency would be (using something simple like a T-test). But I'm not sure how to go about doing that.
On the other hand, is there an easier way to test the probability of obtaining my actual results just given a long file of the results of each random sample?
I assume there's some kind of R function that would make this fairly straightforward. Any hints?