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Luis A. Afonso
Posts:
4,277
From:
LIsbon (Portugal)
Registered:
2/16/05
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Of course NOT Mr. Whitlock . . .
Posted:
Aug 30, 2012 2:25 AM
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M. C. Whitlock, Combining probability from independent tests: the weighted Z-method is superior to Fisher´s approach
J. Evol. Biol. 18 (2005)1368-1375, European Society for Evolutionary Biology.
Reporting
. . . Fisher´s method, however, does have one significant in this context. It treats large and small P-values asymmetrically. It is easiest to see this problem with an example (see Rice 1990). Imagine there were two studies on a topic that we would like to combine. One of these studies rejected the null hypotheses with P=0.001 while the other did not with P=0.999. Clearly, on average there is no consistent effect in these two studies, yet by Fisher´s method the P-value is P=0.008.
Rice W. R. (1990) A consensus combined P-value test and a family-wide significant of components tests. Biometrics 46 303-308.
My Objection
My analysis focuses exclusively the text above. In particular the Rice/Stouffer´s method does not concern me here.
Given that
F = -2*(log .001 + log .999) = 13.8175 and
Prob. Chi2(4) >13.28 = 0.990
I got Rice is thinking at a two-component experiment. Then it is easily shown by simulation that a P-amplitude of two random numbers larger than 0.998 is very unlike: prob.= 4/million approx. So the Null Hypotheses shared by both is completely unacceptable (see text ). It results that Rice´s argument against Fisher´s way falls on earth . . .
Of course if the number of components gets large then is more and more likely they reach large amplitudes.
A crucial point: the P-values follows a uniform Distribution only if we hit *the bulls eye*, the proposed/tentative parameter´s value to test is exactly equal to the Population one. As long I got Whitlock had performed his task following this direction: finding backwards the population p-values by simulation data resampling and drawing conclusions about.
Luis A. Afonso
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