On Sat, May 8, 2010 at 7:02 PM, Bak Kuss <bakk...@gmail.com> wrote: > > Just wondering. > > The smallest the p-value, the closer to 'reality' (the more accurate) > the model is supposed to (not) be (?). > > How realistic is it to be that (un-) real? >
That's a common misconception. A p-value expresses no more than the chance of obtaining the dataset you observe, given that your null hypothesis _and your assumptions_ are true. Essentially, a p-value is as "real" as your assumptions. In that way I can understand what Robert wants to say. But with lare enough datasets, bootstrapping or permutation tests gives often about the same p-value as the asymptotic approximation. At that moment, the central limit theorem comes into play, which says that when the sample size is big enough, the mean is -close to- normally distributed. In those cases, the test statistic also follows the proposed distribution and your p-value is closer to "reality". Mind you, the "sample size" for a specific statistic is not always merely the number of observations, especially in more advanced methods. Plus, violations of other assumptions, like independence of the observations, changes the picture again. The point is : what is reality? As Duncan said, a small p-value indicates that your null hypothesis is not true. That's exactly what you look for, because that is the proof the relation in your dataset you're looking at, did not emerge merely by chance. You're not out to calculate the exact chance. Robert is right, reporting an exact p-value of 1.23 e-7 doesn't make sense at all. But the rejection of your null-hypothesis is as real as life. The trick is to test the correct null hypothesis, and that's were it most often goes wrong... Cheers Joris > bak > > p.s. I am no statistician > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. > -- Joris Meys Statistical Consultant Ghent University Faculty of Bioscience Engineering Department of Applied mathematics, biometrics and process control Coupure Links 653 B-9000 Gent tel : +32 9 264 59 87 joris.m...@ugent.be ------------------------------- Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php [[alternative HTML version deleted]] ______________________________________________ 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.
