On Mar 27, 2014, at 8:14 AM, Hermann Norpois <hnorp...@gmail.com> wrote:
> Hello, > > My main question is wheter my data is distributed normally. As the > shapiro.test doesnt work for large > data sets I prefer the ks.test. > But I have some problems to understand the completely different p-values: > >> ks.test (test, pnorm, mean (test), sd (test)) > > One-sample Kolmogorov-Smirnov test > > data: test > D = 0.0434, p-value = 0.1683 > alternative hypothesis: two-sided > > Warnmeldung: > In ks.test(test, pnorm, mean(test), sd(test)) : > für den Komogorov-Smirnov-Test sollten keine Bindungen vorhanden sein >> shapiro.test (test) > > Shapiro-Wilk normality test > > data: test > W = 0.9694, p-value = 1.778e-10 > > > Generating some random data the difference is acceptable: > >> nt <- rnorm (200, mean=5, sd=1) >> ks.test (nt, pnorm, mean=5, sd=1) > > One-sample Kolmogorov-Smirnov test > > data: nt > D = 0.0641, p-value = 0.3841 > alternative hypothesis: two-sided > >> shapiro.test (nt) > > Shapiro-Wilk normality test > > data: nt > W = 0.9933, p-value = 0.5045 > > > Thanks > hermann <snip> The discussion here (and other similar ones) might be helpful: http://stats.stackexchange.com/questions/362/what-is-the-difference-between-the-shapiro-wilk-test-of-normality-and-the-kolmog You may also be served by searching the R-Help list archives for prior discussions on using normality tests and why they are essentially useless in practice. Regards, Marc Schwartz ______________________________________________ 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.
