[SciPy-User] Questions/comments about scipy.stats.mannwhitneyu
josef.pktd@gmai...
josef.pktd@gmai...
Fri Feb 15 10:58:19 CST 2013
On Fri, Feb 15, 2013 at 11:35 AM, <josef.pktd@gmail.com> wrote:
> On Fri, Feb 15, 2013 at 11:16 AM, <josef.pktd@gmail.com> wrote:
>> On Thu, Feb 14, 2013 at 7:06 PM, Chris Rodgers <xrodgers@gmail.com> wrote:
>>> Hi all
>>>
>>> I use scipy.stats.mannwhitneyu extensively because my data is not at
>>> all normal. I have run into a few "gotchas" with this function and I
>>> wanted to discuss possible workarounds with the list.
>>
>> Can you open a ticket ? http://projects.scipy.org/scipy/report
>>
>> I partially agree, but any changes won't be backwards compatible, and
>> I don't have time to think about this enough.
>>
>>>
>>> 1) When this function returns a significant result, it is non-trivial
>>> to determine the direction of the effect! The Mann-Whitney test is NOT
>>> a test on difference of medians or means, so you cannot determine the
>>> direction from these statistics. Wikipedia has a good example of why
>>> it is not a test for difference of median.
>>> http://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U#Illustration_of_object_of_test
>>>
>>> I've reprinted it here. The data are the finishing order of hares and
>>> tortoises. Obviously this is contrived but it indicates the problem.
>>> First the setup:
>>> results_l = 'H H H H H H H H H T T T T T T T T T T H H H H H H H H H H
>>> T T T T T T T T T'.split(' ')
>>> h = [i for i in range(len(results_l)) if results_l[i] == 'H']
>>> t = [i for i in range(len(results_l)) if results_l[i] == 'T']
>>>
>>> And the results:
>>> In [12]: scipy.stats.mannwhitneyu(h, t)
>>> Out[12]: (100.0, 0.0097565768849708391)
>>>
>>> In [13]: np.median(h), np.median(t)
>>> Out[13]: (19.0, 18.0)
>>>
>>> Hares are significantly faster than tortoises, but we cannot determine
>>> this from the output of mannwhitneyu. This could be fixed by either
>>> returning u1 and u2 from the guts of the function, or testing them in
>>> the function and returning the comparison. My current workaround is
>>> testing the means which is absolutely wrong in theory but usually
>>> correct in practice.
>>
>> In some cases I'm reluctant to return the direction when we use a
>> two-sided test. In this case we don't have a one sided tests.
>> In analogy to ttests, I think we could return the individual u1, u2
>
> to expand a bit:
> For the Kolmogorov Smirnov test, we refused to return an indication of
> the direction. The alternative is two-sided and the distribution of
> the test statististic and the test statistic are different in the
> one-sided test.
> So we shouldn't draw any one-sided conclusions from the two-sided test.
>
> In the t_test and mannwhitenyu the test statistic is normally
> distributed (in large samples), so we can infer the one-sided test
> from the two-sided statistic and p-value.
>
> If there are tables for the small sample case, we would need to check
> if we get consistent interpretation between one- and two-sided tests.
>
> Josef
>
>>
>>>
>>> 2) The documentation states that the sample sizes must be at least 20.
>>> I think this is because the normal approximation for U is not valid
>>> for smaller sample sizes. Is there a table of critical values for U in
>>> scipy.stats that is appropriate for small sample sizes or should the
>>> user implement his or her own?
>>
>> not available in scipy. I never looked at this.
>> pull requests for this are welcome if it works. It would be backwards
>> compatible.
since I just looked at a table collection for some other test, they
also have Mann-Whitney U statistic
http://faculty.washington.edu/heagerty/Books/Biostatistics/TABLES/Wilcoxon/
but I didn't check if it matches the test statistic in scipy.stats
Josef
>>
>>>
>>> 3) This is picky but is there a reason that it returns a one-tailed
>>> p-value, while other tests (eg ttest_*) default to two-tailed?
>>
>> legacy wart, that I don't like, but it wasn't offending me enough to change it.
>>
>>>
>>>
>>> Thanks for any thoughts, tips, or corrections and please don't take
>>> these comments as criticisms ... if I didn't enjoy using scipy.stats
>>> so much I wouldn't bother bringing this up!
>>
>> Thanks for the feedback.
>> In large parts review of the functions relies on comments by users
>> (and future contributors).
>>
>> The main problem is how to make changes without breaking current
>> usage, since many of those functions are widely used.
>>
>> Josef
>>
>>
>>>
>>> Chris
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User@scipy.org
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