BUG: adds warning to scipy.stats.brunnermunzel

[sg-s]

Reference issue

closes #15843

What does this implement/fix?

emits a warning when scipy.stats.brunnermunzel cannot compute p-value
because both numerator and denominator are 0, and therefore p-value is nan
also suggests an alternative (using distribution="normal")

This change does not change any output/no code is changed. The only change
is emitting a warning in some cases where output p-value is nan

also add two tests:

1. test_brunnermunzel_return_nan tests that the correct warning is raised when using a t-distribution
2. test_brunnermunzel_normal_dist tests that p=0 when using a normal dist with the problematic dataset

[mdhaber]

Thanks. If the suggestions above look OK to you, I'll push them, but there's one more thing needed before merge. Can you add your example as a test in scipy/stats/tests/test_stats.py class TestBrunnerMunzel?
Please add a comment about the purpose of the test, mention the original issue (gh-15843), and use with pytest.warns(RuntimeWarning, match='p-value cannot be estimated') to check that the warning is emitted as intended.

[sg-s]

@mdhaber will do, thanks for the review and changes!

[sg-s]

@mdhaber thanks for the suggestions. added your changes, and also added two tests which check for warning and correct p when using a normal distribution. i verified that those two tests pass locally.

[mdhaber]

Thanks! I'd suggest finding an option in your IDE that removes trailing spaces when it saves. Helps avoid PEP8 issues.
I fixed those and added "(see gh-15843)" to the test descriptions. Fingers crossed that CI comes back green! If so, I'll merge.

[sg-s]

thank you! fyi i had to disable my autoformatter (black) because it changed everything else in the file.

also the imports are in a non pep8 order in some of the modules so i had to disable isort too

[mdhaber]

thank you! fyi i had to disable my autoformatter

Thank you for doing that!

[mdhaber]

I'll merge when tests finish.

[mdhaber]

Thanks for the bug report and fix @sg-s! The Main Windows Python310 seems to have hung, as it frequently does, so I'll merge now.
Just curious - what are you using brunnermunzel for, and how did you run across the issue?

[sg-s]

@mdhaber i have a dataset with relatively small n (3-10) where in some cases there was clearly a difference by eye, and t-tests were saying it wasn't different. turns out my data isn't normal, and also is heteroskedastic and what i should have done is a permutation test (which is what i used eventually). but brunnermunzel was one of the things i tried because it seemed to have lower restrictions.

[mdhaber]

Great! Did you use permutation_test to do the permutation test? (I'll fix that issue with nans shortly.) Always good to know how the code is being used. And if you think another test would be good to add, let us know. Thanks!

[sg-s]

yup, ended up using permutation_test