On 1 Dec 2003 at 6:13, A. G. McDowell wrote:

Why not do something even simpler: Iff all the nulls are true, (and 
all distributions are continuous), then all the p-values have a 
uniform(0,1) distribution. Make a QQplot of your p-values against the 
uniform distribution.

Kjetil Halvorsen

> In article <[EMAIL PROTECTED]>, Rich Ulrich
> <[EMAIL PROTECTED]> writes > - I have comments on the question of
> corrections for multiple >testing.  And I'm asking folks for feedback
> on Benjamini >and Hochberg's FDR  as an alternative. > Not strictly
> relevant but there is also Holm-Bonferroni: Given N tests, go through
> the N tail probabilities smallest first, and stop at the rth test if
> its tail probability is > ALPHA/(N-r+1). Reject all null hypotheses
> seen before you stop. So the first probability gets a threshold
> equivalent to the Bonferroni correction, but the following ones get
> treated successively more leniently. Regardless of the number of false
> null hypothesis, the probability of rejecting any true null hypothesis
> is at most ALPHA, because that is the worst case probability of
> failing to stop at the first true null hypothesis. I had a URL for
> this which is now broken, so I'll leave you at the mercy of google. I
> think I saw somewhere that the original paper was getting pretty
> heavily cited.
> 
> Even less relevant is the following curiosity: Suppose that you do N
> tests and take the np - th tail probability. e.g. if p=1/2 you look at
> the median tail probability. If this probability is q, then the
> probability of observing a result of q or less is at most q/p.
> Because: if you pick a single probability at random from amongst the
> N, the probability that this result is <= q is q. If x is the
> probability that Np or more tests are < q, then we have q >= x * p +
> (1-x) * 0, so x <= q/p. The catch with this one is that you don't get
> to find out WHICH individual test is causal. -- A. G. McDowell . .
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