Alan:
I don't understand your comments about the estimation of a proportion.
It sounds to me as if you are using the estimated standard error. (Surely
you are not assuming a known standard error.) You are presumably, also
using the normal approximation to the binomial (or perhaps the
hypergeometric.)
To do so requires a "large" sample size in which case it doesn't matter
whether
you use the normal or t distribution. Both would be acceptable approximations.
(and both would be approximations.) So what is your point?
Once more I think you need to separate the issues of what statistic to use
and what distribution to use.
Jon
At 01:10 PM 4/20/01 -0400, you wrote:
>(This note is largely in support of points made by Rich Ulrich and
>Paul Swank.)
>
snip
>
>Now consider estimation of a proportion. Using the information that the
>data consist only of 0's and 1's, and an approximate value of the
>proportion, we can calculate an approximate standard error more
>accurately (for p near 1/2) than we could without this information. The
>interval based on the usual variance formula p(1-p) and the z
>distribution is therefore better than the one based on the t
>distribution. This is why (as Paul pointed out) everybody uses z
>tests in comparing proportions, not t tests. The same applies to
>generalizations of tests of proportions as in logistic regression.
>
>snip
>
> Alan Zaslavsky
> Harvard Med School
>
>
>
>=================================================================
>Instructions for joining and leaving this list and remarks about
>the problem of INAPPROPRIATE MESSAGES are available at
> http://jse.stat.ncsu.edu/
>=================================================================
>
>
=================================================================
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
http://jse.stat.ncsu.edu/
=================================================================
