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List:       jakarta-commons-dev
Subject:    [jira] [Commented] (MATH-1381) BinomialTest P-value > 1
From:       "Kexin Xie (JIRA)" <jira () apache ! org>
Date:       2016-08-31 19:34:20
Message-ID: JIRA.12989690.1468564132000.461487.1472672060610 () Atlassian ! JIRA
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    [ https://issues.apache.org/jira/browse/MATH-1381?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15453154#comment-15453154 \
] 

Kexin Xie commented on MATH-1381:
---------------------------------

Hi [~erans], thanks for looking at the PR. I agree with you that this does seems like \
it's a dirty fix and mask a potential bug in the computation.

However, the main problem here is that there is one corner case that the current \
algorithm did not consider. Which is that if the probability is large enough and the \
success is the same as the number of trials and both numbers are small enough, it \
will cause the {{criticalValueLow}} to rise too quickly and be the same as \
{{criticalValueHigh}}. The if condition in L138 is suppose to check the symmetry case \
when {{pLow == pHigh}}, but is not for the case when {{criticalValueLow == \
criticalValueHigh}}. At that point the probability will always jump to above 1.

It may seem like a dirty fix, but I have checked against results in R, and Python's \
scipy equivalent, and they produce the same value. I implemented this way because it \
actually works in handling this boundary condition, and it's the least change to the \
original implementation. Note that Python's scipy also uses a similar approach to \
deal with estimated value rising above 1 \
https://github.com/scipy/scipy/blob/v0.14.0/scipy/stats/morestats.py#L1661

I've also updated with more exhaustive test cases, please have a look again.

> BinomialTest P-value > 1
> ------------------------
> 
> Key: MATH-1381
> URL: https://issues.apache.org/jira/browse/MATH-1381
> Project: Commons Math
> Issue Type: Bug
> Reporter: Wang Qiang
> 
> When I use the Binomial Test, I got p-value > 1 for two sided check.
> Example:
> (new BinomialTest()).binomialTest(200, 200, 0.9950429, \
> AlternativeHypothesis.TWO_SIDED) == 1.3701357550780435 In my case, if the expected \
> p-value is 1 (calculated by package in other language, scipy in this case), the \
> p-value returned could be > 1



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