Granted I am a hack who teaches intro stats (occasionally intermediate level) 
and not an actual research scientist but I always like to joke (and keep my 
students awake) by exclaiming that "Size doesn't count...except in statistical 
sample size." and (of course) 'Bigger IS better."
 
^ -
 
Nancy Melucci
Long Beach City College
Long Beach CA
 
 
-----Original Message-----
From: David Epstein <da...@neverdave.com>
To: Teaching in the Psychological Sciences (TIPS) <tips@fsulist.frostburg.edu>
Sent: Sun, Jun 15, 2014 10:20 am
Subject: Re: [tips] Sample sizes


On Sun, 15 Jun 2014, Annette Taylor went:

> Now I am reading one of several papers on newer method in stats and
> it appears that much greater sample sizes are in order.  A paper in
> Perspectives on Psychological Sciences is even advocating samples
> over 500 if I would want 90 percent power.  Back in the day that
> would have been denounced as the very worst kind of stats thinking.

Quick answer: Overpowering a study would indeed be a problem if you
were going to base your conclusions on whether a null hypothesis could
be rejected at some particular alpha level.

But recent criticism of studies with too-small sample sizes has gone
hand in hand with criticism of that "null-hypothesis significance
testing" approach.  The recommendation I typically see in these
critiques is that you should formulate your hypothesis in terms of an
effect size (which might itself reflect the ABSENCE of an appreciable
difference, as in, "The difference in IQ between men and women should
be no more than d =
Some-Minuscule-Number-With-No-Practical-Significance") and then power
your study so the 95% confidence interval is nice and narrow.  With
that approach, bigger is generally better, because there's not really
any such thing a false positive--just more and more precision.

--David Epstein
   da...@neverdave.com

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