You still are stating the effect of the central limit theorem incorrectly. Please see my previous note.
Frank On 06/25/2010 10:27 AM, Joris Meys wrote: > 2010/6/25 Frank E Harrell Jr<f.harr...@vanderbilt.edu>: >> The central limit theorem doesn't help. It just addresses type I error, >> not power. >> >> Frank > > I don't think I stated otherwise. I am aware of the fact that the > wilcoxon has an Asymptotic Relative Efficiency greater than 1 compared > to the t-test in case of skewed distributions. Apologies if I caused > more confusion. > > The "problem" with the wilcoxon is twofold as far as I understood this > data correctly : > - there are quite some ties > - the wilcoxon assumes under the null that the distributions are the > same, not only the location. The influence of unequal variances and/or > shapes of the distribution is enhanced in the case of unequal sample > sizes. > > The central limit theory makes that : > - the t-test will do correct inference in the presence of ties > - unequal variances can be taken into account using the modified > t-test, both in the case of equal and unequal sample sizes > > For these reasons, I would personally use the t-test for comparing two > samples from the described population. Your mileage may vary. > > Cheers > Joris > >> >> On 06/25/2010 04:29 AM, Joris Meys wrote: >>> As a remark on your histogram : use less breaks! This histogram tells >>> you nothing. An interesting function is ?density , eg : >>> >>> x<-rnorm(250) >>> hist(x,freq=F) >>> lines(density(x),col="red") >>> >>> See also this ppt, a very nice and short introduction to graphics in R : >>> http://csg.sph.umich.edu/docs/R/graphics-1.pdf >>> >>> 2010/6/25 Atte Tenkanen<atte...@utu.fi>: >>>> Is there anything for me? >>>> >>>> There is a lot of data, n=2418, but there are also a lot of ties. >>>> My sample n≈250-300 >>> >>> You should think about the central limit theorem. Actually, you can >>> just use a t-test to compare means, as with those sample sizes the >>> mean is almost certainly normally distributed. >>>> >>>> i would like to test, whether the mean of the sample differ significantly >>>> from the population mean. >>>> >>> According to probability theory, this will be in 5% of the cases if >>> you repeat your sampling infinitly. But as David asked: why on earth >>> do you want to test that? >>> >>> cheers >>> Joris >>> >> >> >> -- >> Frank E Harrell Jr Professor and Chairman School of Medicine >> Department of Biostatistics Vanderbilt University >> > > > -- Frank E Harrell Jr Professor and Chairman School of Medicine Department of Biostatistics Vanderbilt University ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
