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From Philippe Mouawad <>
Subject Re: [GitHub] jmeter issue #296: Bug 61078 - Percentile calculation error
Date Sun, 28 May 2017 09:51:09 GMT
After reading further on this topic and also reading the different
comments, my position would be:
- switch everywhere to R1 (also in commons-math)
- use the PR from contributor for the median and jorphan computations
- document the change and algo somewhere

>From my understanding, tests having large results should not be affected by

This would at least make computations uniform until we decide what library
to use.

I need your go before going further.

If we decide for statusquo then please comment on respective bugs to
explain to reported and contributor why we won't change anything.


On Tuesday, May 9, 2017, Felix Schumacher <>

> Am 09.05.2017 09:11, schrieb pmouawad:
>> Github user pmouawad commented on the issue:
>>     Hello @abalanonline ,
>>     Thanks for your replies and explanations !
>>     I am not a math expert as you seem to be, so I have few questions
>> you may be able to help on:
>>     1. Thanks to your comment, I see default method is LEGACY, and the
>> one you have created is R_1. Do you have some insights on the
>> different method and their limits / use cases ?
>>     2. Why does the "bug" you report affect all libraries I checked
>> (HdrHistogram, and JOrphan ) ?
>> Can't it be due to a different method estimation algorithm ?
>>     Note I share your thoughts on using a dedicated library but
>> commons-math may be overkill in terms of performance compared to
>> HdrHistogram or t-digest.
> I have tried to do a bit of research on percentiles, quantiles and median.
> It looks to me, that those "points" are more like ranges, and there is no
> exact value.
> R and numpy will interpolate the median and the percentiles/quantiles. The
> statistics module
> of python 3 has three different median implementations called median,
> median_high and median_low,
> that interpolate, give the highest possible median and the lowest.
> Wikipedia (the german one), gives a definition of an "Empirisches
> Quantile" (empiric quantile),
> where it settles on the lower border of the quantiles (and therefore the
> median).
> I wonder if we should change our implementation at all.
> Felix
>>     Thanks
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Philippe Mouawad.

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