[patch #5583] NPAR TESTS

[John Darrington]

Update of patch #5583 (project pspp):

                  Status:            Works For Me => Ready For Test/Review  
             Assigned to:                     jmd => jstover                

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Follow-up Comment #10:

>   Pr ({less than or equal to 10 males} \cup {more than 10 males})

>    ...which is 1.0 for a binomial random variable with 20 trials 
>    and null hypothesis success probability of 0.5. 

Isn't it also 1.0 for ANY hypothesis ??  Some of the finer issues seem
counter-intuitive to me.  This explains why I'm not a statistician.  However
....

A new patch is attached, which compiles properly against the latest HEAD.  
In this patch, I've used the  gslextras_cdf_binomial_P.  I've also dropped
the optimisation which sets m = MIN(n1, n2) and p  = m == n1?p : 1 -p because
this should be done inside the cdf function --- I don't know if it actually
is?

I've changed the code to make the way it works more obvious, and I've clamped
the 2 tailed significance to 1.0 --- and now, the exact tests give the same
results as those of the Chicago Company.

The assymptotic tests however, are interesting, and I have come to this
conclusion about what spss actually does:

If n1 + n2 > 25, then spss labels its reported value as an asymptotic result,
BUT this is a lie, and it's actually an exact result.  Probably there is some
threshold above which they do actually calculate asymptotic results, but most
likely it's well above 25.

So I suppose the question is, do we try to emulate these misleading results,
or go with correct ones?







(file #11535)
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Additional Item Attachment:

File name: npar4.patch                    Size:270 KB


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