The central limit theorem doesn't help. It just addresses type I error, not power.
Frank 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 ______________________________________________ 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.
