In the context of your problem as phrased, I'd suggest you look at
sampling plans & inspection sequences.

You have an inspection (a test procedure) that results in identification,
P1 exists, or P1 does not exist.  You have an alpha risk and a beta risk.
(the % incorrectly identified P1 may = alpha + beta, depending on how you
define things).

If I inspect a lot of material, I can have an inspection sequence where I
sample n pieces, determining in each case with absolute certainty that
condition P1 exists in any given piece.  for a given sample size, n, I
can calculate an OC (operating characteristic) curve, from which I
determine the likelihood of establishing the acceptability or reject
ability of the entire lot.  Or I could use the MIL standards (now with a
new ISO name) to specify the minimum sample size and number of P1
detects.

I think a strong cognate analytical structure could be built.  If so,
then sampling plans will solve your problem.

I suppose you could also report % correct ID (subject to the alpha + beta
issue above), and compare two methods using a t test using proportions.
I feel less comfortable about this, if you do not examine alpha & beta
risks separately.

Jay

JGR wrote:

> Hi,
>
> I have the following problem:
> I need to select between test procedures T1 and T2. Previous
> observations show that T1 correctly identified problem P1 in 29 out of
> 30 cases and T2 correctly identified problem P1 in 5 out of 5 cases. I
> want to have a test procedure selection measure, which represents both
> accuracy of the procedure and frequency of previous observations.
>
> Thanks,
> JGR
> .
> .
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--
Jay Warner
Principal Scientist
Warner Consulting, Inc.
4444 North Green Bay Road
Racine, WI 53404-1216
USA

Ph: (262) 634-9100
FAX: (262) 681-1133
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