Uwe Ligges <[EMAIL PROTECTED]> writes:
> > A = 0.1846, p-value = 0.9059 > > > >> ad.test(rnorm(100,mean=5,sd=3)) > > > > ... > > A = 0.5138, p-value = 0.1887 > > > > I mistakenly had thought the p-values would be more stable since I > > am artificially creating a random normal distribution. Is this expected > > for a normality test or is this an issue with how rnorm is producing > > random numbers? I guess if I run it many times, I would find that I > > would get many large values for the p-value? > > Well, as many large values as small values, 5% significant differences > for the 5% level.... > > The following looks alright: > > hist(replicate(1000, ad.test(rnorm(100,mean=5,sd=3))$p.value)) We see this misunderstanding worryingly often. Worrying because it reveals that a fundamental aspect of statistical inference has not been grasped: that p-values are designed to be (approximately) uniformly distributed and fall below any given level with the stated probability, when the null hypothesis is true. There is no mechanism to give you "fewer significant" or "more stable" p-values, and a p-value close to one is no better an indication of a true null hypothesis than one of 0.5 or 0.25. -- O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
