[Rd] Bias in R's random integers?
Philip B. Stark
@t@rk @ending from @t@t@berkeley@edu
Wed Sep 19 18:18:18 CEST 2018
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.duncan using gmail.com>
> On 19/09/2018 9:40 AM, David Hugh-Jones wrote:
> > On Wed, 19 Sep 2018 at 13:43, Duncan Murdoch <murdoch.duncan using gmail.com
> > <mailto:murdoch.duncan using 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 |
[[alternative HTML version deleted]]
More information about the R-devel