>
> Am 23.09.2008 um 23:57 schrieb Peter Dalgaard:
>
>> For this kind of problem I'd go directly for the binomial
>> distribution. If the actual probability is 0, this is essentially
>> deterministic and you can look at
>>
>> > binom.test(0,99,p=.03, alt="less")
>>
>
> > This means that you don't sample from the p=.03 population?
>> Note that there is a 5 per cent chance to have 0 failures in 99
> > trials with p=.03.
> Yes, that's what I read the task as saying: Sample from p=0.00 when the
> hypothesis is p=0.03. Then rejection happens with probability 1 when n
> >= 99. Actually, he said that we could assume the _sample_ rate to be
> 0%, but that is only assured when p=0.0.
>
> (You can continue the game by looking at the probability of getting 0
> failures, depending on the true p. E.g., if p=0.001, we have
>
> > dbinom(0, 99, 0.001)
> [1] 0.9056978
>
> i.e. 90% power to detect at 5% level. And further continue into a full
> power analysis where you calculate the probability of a failure rate
> that is significantly different from 0.03 depending on p and n.)
So my task is to compare a single sample population (which should exhibit
0 successes, against an a-priori p value with the goal of asking if the
sample is consistent with a p-value of that or less.
My goal is to determine the number of sample points needed to state with
95% confidence that the sample is predictive at alpha=.05. The process in
question should always return 0 successes but I need to know how many to
test so as to make those 0 successes meaningful.
If I understand you correctly Peter then the binom.test procedure with a
specified p value will do. Or rather that:
> binom.test(0, 99, p=0.01, alt="less", conf.level=0.99)
Exact binomial test
data: 0 and 99
number of successes = 0, number of trials = 99, p-value = 0.3697
alternative hypothesis: true probability of success is less than 0.01
99 percent confidence interval:
0.00000000 0.04545154
sample estimates:
probability of success
0
correctly informs me that at a 99% confidence interval 99 systems is
woefully inadequate.
Thanks,
Collin Lynch.
Thanks,
Collin Lynch.
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