FWIW (It may not be much): 1. Data **are never** Gaussian. Failure to reject the hypothesis that the data are sampled from a Gaussian does not mean that the data can be assumed to be drawn from a Gaussian. That depends on the statistical methodology and the application context.
2. Given a large enough sample, one will always reject the hypothesis that the data are drawn from a Gaussian. That does not mean that nevertheless making that assumption will result in any problems. That depends on the statistical methodology and the application context. 3. Mostly 1) and 2) are of little import anyway, despite what the statistical texts say. Much more important in practice -- and the source of much grief and many irreproducible results -- is the "i.i." of iid. ... aka unknown exogenous systematic effects, measurement biases, clustering ... 4. I know this is OT, and I apologize. I also know that this is just my 2 cents opinion -- and probably not really worth even that much -- so feel free to dismiss. Also, if you care to reply or argue, please do so off list. I will not defend anything I've said on list. I've already got an apartment reserved for me in one of Pat Burns's "R Inferno" levels, and I don't want to descend even further. Best, Bert ______________________________________________ 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.