On Sun, 4 Dec 2016, Allin Cottrell wrote:
It seems that under the null the p-value ought to be distributed
uniformly on
(0,1). That appears to the case for the chi-square test, but not at all for
the two tests that employ the inverse normal transformation.
The way I see it, the series z you're generating in the "cdftest" function
is not really normally distributed. Rather, is constructed in a way such
that its frequency distribution resembles a Gaussian density, which
wouldn't be guaranteed if data were truly normal. In other words, your
normals are "too good to be true"; hence, your p-values are mostly very
close to 1.
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Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
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