From: Rolf Turner <rolf_at_math.unb.ca>

Date: Tue 13 Jul 2004 - 05:29:57 EST

https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Jul 13 05:41:54 2004

Date: Tue 13 Jul 2004 - 05:29:57 EST

There has been a plethora of responses over the past hour or so to a question posed by Darren Shaw about how to estimate (get a confidence interval for) a proportion based on a data set consisting of a number of proportions. These responses have been all off the point. I would suggest to the responders:

** RTFQ
**
The question was not about how to calculate a confidence interval for
a proportion. Responders have gone on and on with academic wanking
about alternatives to the ``standard'' procedure, some of which give
better coverage properties (and some of which don't; so-called
``exact'' methods are notoriously bad).

The point of the question was how to combine the information from a number of (sample) proportions. If the structure and context are as I conjectured in my posting then

(a) this is simple, and

(b) the combined sample size is almost surely large enough so

that the simple and easy standard procedure will produce an eminently adequate result. (Thus making the alternative approaches even more of an academic wank than they usually are.) I think at this point it is worthwhile repeating the quote posted a while back by Doug Bates. (He attributed the quote to George Box, but was unable to supply a citation; I wrote to Box asking him about the quote, and he said ``Nope. 'Twarn't me.'') But irrespective of the source of the quote, the point it makes is valid: ``You have a big approximation and a small approximation. The big approximation is your approximation to the problem you want to solve. The small approximation is involved in getting the solution to the approximate problem.''

That is to say there are ***many*** effects which will have an impact on the proportion estimate required. (Were the samples really random? Were they really independent? Were they really all taken from the same population or populations with the same sample proportion?) The impact of such considerations causes the issue of the roughness of the usual/standard approximate CI for a proportion to pale by comparison.

cheers,

Rolf Turner rolf@math.unb.ca ______________________________________________R-help@stat.math.ethz.ch mailing list

https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Jul 13 05:41:54 2004

*
This archive was generated by hypermail 2.1.8
: Wed 03 Nov 2004 - 22:54:52 EST
*