That depends on the number of replications, among other things. Moreover, because of the bias, the usual formulae for uncertainty in estimates based on random samples, etc., are incorrect: sample() does not give a simple random sample.
On Wed, Sep 19, 2018 at 9:15 AM Duncan Murdoch <murdoch.dun...@gmail.com> wrote: > On 19/09/2018 9:40 AM, David Hugh-Jones wrote: > > > > > > On Wed, 19 Sep 2018 at 13:43, Duncan Murdoch <murdoch.dun...@gmail.com > > <mailto:murdoch.dun...@gmail.com>> wrote: > > > > > > I think the analyses are correct, but I doubt if a change to the > > default > > is likely to be accepted as it would make it more difficult to > > reproduce > > older results. > > > > > > I'm a bit alarmed by the logic here. Unbiased sampling seems basic for a > > statistical language. As a consumer of R I'd like to think that e.g. my > > bootstrapped p values are correct. > > Surely if the old results depend on the biased algorithm, then they are > > false results? > > All Monte Carlo results contain Monte Carlo error. Using the biased > function will have some additional error, but for almost all > simulations, it will be negligible compared to the Monte Carlo error. I > suspect the only simulations where the bias was anywhere near the same > order of magnitude as the Monte Carlo error would be ones designed with > this specific code in mind. > > Duncan Murdoch > > -- Philip B. Stark | Associate Dean, Mathematical and Physical Sciences | Professor, Department of Statistics | University of California Berkeley, CA 94720-3860 | 510-394-5077 | statistics.berkeley.edu/~stark | @philipbstark [[alternative HTML version deleted]] ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
