On Mon, 5 Dec 2016, Sven Schreiber wrote:
> 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.
Jack, I know you must mean something else than what you've written -- the
data's density "too" Gaussian to be Gaussian??
That's _exactly_ what I meant. The histogram of the values generated by
Allin's script are so neatly aligned along the Gaussian density that the
p-values of any normality tests are much more often close to 1 than they
would be if the data were truly Gaussian. You can see this in a different
perspective as a lack of independence between observations.
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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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