[R-sig-ME] P value value for a large number of degree of freedom in lmer

Rolf Turner r.turner at auckland.ac.nz
Wed Nov 24 01:25:15 CET 2010

On 24/11/2010, at 1:09 PM, Jonathan Baron wrote:

> For the record, I have to register my disagreement.  In the
> experimental sciences, the name of the game is to design a
> well-controlled experiment, which means that the null hypothesis will
> be true if the alternative hypothesis is false.  People who say what
> is below, which includes almost everyone who responded to this post,
> have something else in mind.  What they say is true in most
> disciplines.  But when I hear this sort of thing, it is like someone
> is telling me that my research career as an EXPERIMENTAL psychologist
> has been some sort of delusion.
> If you have a very large sample and you are doing a correlational
> study, yes, everything will be significant.  But if you do the kind of
> experiment we struggle to design, with perfect control conditions, you
> won't get significant results (except by chance) if your hypothesis is
> wrong.

	I'll bet you don't work with samples of size 200,000. :-)

	Also I'll bet that you don't ***really*** care if the
	difference between mu_T and mu_C is bigger than 0.000001 mm,
	say, whereas you might care if the difference were bigger than
	10 mm.

	Also there's no such thing as ``perfect'' anything, let alone
	control conditions.


			Rolf Turner

> Jon
> On 11/24/10 07:59, Rolf Turner wrote:
>> 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.

More information about the R-sig-mixed-models mailing list