On 11 August in a thread about -How to determine adequate samples Ken
Mintz <[EMAIL PROTECTED]> wrote:
The std err (se) around the mean is given by the formula:
se = sd / sqrt(n) (68% conf)
se = (1.96*sd) / sqrt(n) (95% conf)
se = (2.58*sd) / sqrt(n) (99% conf)
where sd is the std dev. Suppose you want to be 99% that the
population
avg is within +/-3% of the sample avg. Then, se = 0.03*avg. (We
choose
3% or whatever arbitrarily.) Then the minimum sample size (n) is:
x = (0.03*avg) / (2.58*sd)
n = x*x
My question is - Is it sensible to use this formula to do something
else?
Situation: 35 'examiners' award a percentage score for the performance
of examinees. In the course of a year each examiner will see about 500
examinees, perhaps 30 or so in a single session.
I'm using Microsoft Access Database to *enter* the scores awarded (not
to analyse it! - that's to be done using Minitab). I want to set up a
'rule' in Access that indicates that there is less than 99% certainty
that a session is +/- 3% (or similar arbitrary cut off points) from a
'gold standard'.
(e.g. to compare the session mean for a single examiner with - A the
grand mean for all that examiners scores and - B, the session mean for
that single examiner with the grand mean for the population of
examiners. The basic question is - Are A & B within the +/- 3% bounds?
If yes = accept, if no = check and adjust).
Help and advice appreciated.
Jonathan Robbins
J H Robbins FRSA FRPS posting from Dorset in the UK.
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