I agree but how to test that a significant result is not due to the amount
of data but by a real effect.
I though about subsetting my dataset and rerun the model X time to see if
the result still persist ... but you can also say that doing so I will
achieve to find a (small enough) size of subset at which I will not detect
the effect :-)
I also agree that the term "bias" was not correctly used ... but is there a
method to increase the confidence in those results ?
cheers,
Arnaud
2010/11/23 Rolf Turner
>
> It is well known amongst statisticians that having a large enough data set
> will
> result in the rejection of *any* null hypothesis, i.e. will result in a
> small
> p-value. There is no ``bias'' involved.
>
> cheers,
>
> Rolf Turner
>
> On 24/11/2010, at 4:06 AM, Arnaud Mosnier wrote:
>
> > Dear UseRs,
> >
> > I am using a database containing nearly 200 000 observations occurring in
> 33
> > groups.
> > With a model of the form ( y ~ x + (1|group) ) in lmer, my number of
> degree
> > of freedom is really large.
> > I am wondering if this large df have an impact on the p values, mainly if
> > this could conduct to consider the effect of a variable as significant
> while
> > it is not .
> > ... and if it is the case, does it exist a correction to apply on the
> > results to take into account that bias.
> >
> > thanks !
> >
> > [[alternative HTML version deleted]]
> >
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>
>
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