summaryrefslogtreecommitdiff
path: root/java/git@git.pankzsoft.net:lambda-nantes
diff options
context:
space:
mode:
authorArnaud Bailly <arnaud@pankzsoft.com>2026-08-31 16:27:17 +0200
committerArnaud Bailly <arnaud@pankzsoft.com>2026-09-01 07:59:01 +0200
commit19c352276211106884078495a2244fe454f92cbe (patch)
tree915d36b906acbde5b5fb29e48a647590b2dca91e /java/git@git.pankzsoft.net:lambda-nantes
parent32f9be77d18d3ad0cb86e7290c6df7c51ab99ca5 (diff)
downloadlambda-nantes-19c352276211106884078495a2244fe454f92cbe.tar.gz
Fix test for uniformity of distribution for generation of integers
This lead me into a rabbit hole to understand how one should assert the shape of the distribution of some empirical data w.r.t. to the "true" expected distribution which uses the Pearson coefficient test. Unfortunately for very large number of samples deviations sum up quickly to a point where the computed p-value is basically 0, thus rejecting the null hypothesis. While this is theoretically true, there are a lot of ways to introduce biases that will lead to rejection, one of them being the use of modulus to select numbers from the generator's output!
Diffstat (limited to 'java/git@git.pankzsoft.net:lambda-nantes')
0 files changed, 0 insertions, 0 deletions