The computation 2*sum(dbinom(c(10:25),25,0.061)) does not correspond
to any reasonable definition of p-value. For a symmetric
distribution, it is fine to use 2 times the tail area of one tail.
For an asymetric distribution, this is silly.
The standard definition given in elementary texts is usually somthing like
the probability of observing a test statistic at least as
extreme as the observed value
or more formally as
the smallest significance level at which the observed result would
lead to rejection of the null hypothesis
Either definition requires further decisions (what does "at least as
extreme" mean?). In an asymetric distribution, "at least as far from
E(X|H0)" is not a good interpretation, since deviations in one direction
may be much less probable than deviations in the other direction.
Peter's interpretation corresponds both to the interpretation of "at
least as extreme" as "at least as improbable", and also to the
"smallest significance level" interpretation for the test implemented
in binom.test, ie the Clopper-Pearson "exact" test. 2 times the upper
tail area corresponds to neither. The fact that it is implemented in
SAS and appears in a text do not rescue it from that fundamental
failure to make sense.
albyn
On Thu, Feb 05, 2009 at 09:48:11PM +0100, Peter Dalgaard wrote:
> Michael Grant wrote:
>> I believe the binom.test procedure is producing one tailed p values
>> rather than the two tailed value implied by the alternative hypothesis
>> language. A textbook and SAS both show 2*9.94e-07 = 1.988e-06 as the
>> two tailed value. As does the R summation syntax from R below. It
>> looks to me like the alternative hypothesis language should be revised
>> to something like " ... greater than or equal to ..." Am I mistaken?
>
>
> Yes. Or maybe, it is a matter of definition. The problem is that
>
> > (0:25)[dbinom(0:25,25,.061) <= dbinom(10,25,.061)]
> [1] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
>
> so with R's definition of "more extreme", all such values are in the upper
> tail.
>
> Actually, if you look at the actual distribution, I think you'll agree that
> it is rather difficult to define a lower tail with positive probability
> that corresponds to X >= 10.
>
> > round(dbinom(0:25,25,.061),6)
> [1] 0.207319 0.336701 0.262476 0.130726 0.046708 0.012744
> [7] 0.002760 0.000487 0.000071 0.000009 0.000001 0.000000
> [13] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
> [19] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
> [25] 0.000000 0.000000
>
> In any case, you would be hard pressed to find a subset of 0:25 that has
> the probability that SAS and your textbook claims as the p value.
>
>
>>
>>
>> M.C.Grant
>>
>>
>>> 2*sum(dbinom(c(10:25),25,0.061))
>>
>> [1] 1.987976e-06
>>
>>
>>> binom.test(10,25,0.061)
>>
>>
>> Exact binomial test
>>
>>
>> data: 10 and 25
>> number of successes = 10, number of trials = 25, p-value = 9.94e-07
>>
>> alternative hypothesis: true probability of success is not equal to
>> 0.061
>> 95 percent confidence interval:
>>
>> 0.2112548 0.6133465
>> sample estimates:
>>
>> probability of success
>> 0.4
>>
>>
>> [[alternative HTML version deleted]]
>>
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>> and provide commented, minimal, self-contained, reproducible code.
>
>
> --
> O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
> c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
> (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
> ~~~~~~~~~~ - (p.dalga...@biostat.ku.dk) FAX: (+45) 35327907
>
> ______________________________________________
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