Hi Martin, I take your point - but I'd argue that significance stars are a clumsy solution to the very real problem that you outline, and their inclusion as a default sends a signal about their appropriateness that I would prefer R not to endorse.
My preference (to the extent that it matters) would be to see the significance stars be an option but not a default one, and the addition of different functionality to handle the many-predictor problem, perhaps a new summary that more efficiently provides more useful information. If we were to invent lm() now, how would we solve the problem of big P? I don't think we would use stars. Cheers, Andrew On Thu, 28 Mar 2019 at 20:19, Martin Maechler <maech...@stat.math.ethz.ch> wrote: > >>>>> Lenth, Russell V > >>>>> on Wed, 27 Mar 2019 00:06:08 +0000 writes: > > > Dear R-Devel, As I am sure many of you know, a special > > issue of The American Statistician just came out, and its > > theme is the [mis]use of P values and the many common ways > > in which they are abused. The lead editorial in that issue > > mentions the 2014 ASA guidelines on P values, and goes one > > step further, by now recommending that the words > > "statistically significant" and related simplistic > > interpretations no longer be used. There is much > > discussion of the problems with drawing "bright lines" > > concerning P values. > > > This is the position of a US society, but my sense is that > > the statistical community worldwide is pretty much on the > > same page. > > > Meanwhile, functions such as 'print.summary.lm' and > > 'print.anova' have an argument 'signif.stars' that really > > does involve drawing bright lines when it is set to > > TRUE. And the default setting for the "show.signif.stars" > > option is TRUE. Isn't it time to at least make > > "show.signif.stars" default to FALSE? And, indeed, to > > consider deprecating those 'signif.stars' options > > altogether? > > Dear Russ, > Abs has already given good reasons why this article may well be > considered problematic. > > However, I think you and (many but not all) others who've raised > this issue before you, slightly miss the following point. > > If p-values are misleading they should not be shown (and hence > the signif.stars neither. > That has been the approach adopted e.g., in the lme4 package > *AND* has been an approach originally used in S and I think > parts of R as well, in more places than now, notably, e.g., for > print( summary(<glm>) ). > > Fact is that users will write wrappers and their own packages > just to get to p values, even in very doubtful cases... > But anyway that (p values or not) is a different discussion > which has some value. > > You however focus on the "significance stars". I've argued for > years why they are useful, as they are just a simple > visualization of p values, and saving a lot of human time when > there are many (fixed) effects looked at simultaneously. > Why should users have to visually scan 20 or 50 numbers? In > modern Data analysis they should never have to but rather look > at a visualization of those numbers. ... and that's what > significance stars are, not more, nor less. > > Martin > > ______________________________________________ > R-devel@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel > > [[alternative HTML version deleted]] ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
