Not sure that I've phrased the question correctly. If you have a series of p values from a series of tests, and they're all meant to be uniformly distributed, why do you have to do a KS test on that, and not another Chi-squared test?(adsbygoogle = window.adsbygoogle || []).push({});

The following is an extract from a test program's output:-

Test no. 1 p-value .886973

Test no. 2 p-value .473563

Test no. 3 p-value .358962

Test no. 4 p-value .894858

Test no. 5 p-value .767457

Test no. 6 p-value .583446

Test no. 7 p-value .227626

Test no. 8 p-value .765091

Test no. 9 p-value .298747

Test no. 10 p-value .108371

Results of the OSUM test for pu256.bin

KSTEST on the above 10 p-values: .059581

The p-values are meant to be uniformly distributed across 0.0 to 1.0. This implies that they should all be 0.5ish. Why doesn't the program (Diehard randomness tester) perform a Chi-squared test on the ps? This happens several times in the complete report, so I take it to be deliberate. It's always a KS test on uniformly distributed ps. Isn't the Chi-squared test numerically simpler too?

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# Can a Chi2 test be used on uniform p test results?

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