the purpose and any inferential statistical procedure is to either answer
the question: what is the parameter, or ... to test some specific
hypothesis ABOUT a parameter ...
thus, the goal of inferential statistics IS finding the parameter.
now, significance is nothing more than asking what is the p value for
getting some STATISTIC IF the parameter had been some value ... so, in
essence, significance testing is about finding the parameter
larger sample sizes (random i hope) get us closer to the answer we want ...
what is the parameter or ... what is it NOT
in this context, you can't have a sample that is too large since, it will
systematically get you closer and closer to the truth ... which is what are
goal is ...
the title of this post should not be ... Sample size: way toooooo big ...
but, sample size ... do we need a HUGE sample size in all cases to answer
the question that we have about the parameter ... in that context, the
answer is clearly no
At 11:01 AM 03/22/2000 +0000, P.G.Hamer wrote:
>DeLa wrote:
>
> > I have been trying to explain to some co-workers that a sample
> > can be too big.
> > That is not very easy because it is contratictory to what
> > intuition says.
> >
> > Can someone point me to some good arguments or literature?
> > Or correct me if my assumption is wrong?
>
>I can see that huge sample might create problems by making
>some sorts of analysis too computationally demanding.
>
>A large sample will also expose the absurdity of some tests of
>significance.
>
>Can you explain your thinking?
>
>Peter
>
>
>
>
>
>
>
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