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I expect that, in Python with numpy and scipy,
will return P-value which is very close to 1.
In reality, I constantly get different P-values which are not close to 1 at all. P-values of different runs are scattered from 0 to 1, no matter how many categories I have and how big sample is.
It seems I don't understand something very basic. But what? Is chi2 test applicable here?
If not, how can I test uniformity of pseudo-random values generated by machine? (I know that there are a lot of tests for random number generators. I'm looking for test for uniformity.)
What I didn't understand
Indeed, P-value is distributed uniformly if null-hypothesis is true. Looks obvious when I understood it.
My mistake was that I expected to have P-value to be close to 1. If distribution is really uniform, P-value should be uniformly scattered over [0, 1]. And I should check that P-value greater than some value close to 0. If distribution weren't uniform, I would get values close to 0, since there is very low (close to 0) probability that I would get more extreme results if distribution were uniform.