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Re: st: Very high t- statistics and very small standard errors


From   Maarten Buis <maartenlbuis@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Very high t- statistics and very small standard errors
Date   Tue, 1 May 2012 11:17:29 +0200

On Tue, May 1, 2012 at 3:18 AM, Laurie Molina wrote:
> It is not the first time I hear people say that when you have a lot of
> observations everything is significant... Is it because the lenght of
> the confidence intervals is inversely related to the number of
> observations considered? Or could you tell me what is the logic behind
> saying that with a lot of observations everything is statistically
> significant?

The logic is that statistical testing is all about the random
variation in your coefficients you would expect due to the fact your
data is a random sample of the population. You would expect that if
you draw a sample you would not find exactly the same statistic as you
would expect under the null hypothesis even if the null hypothesis is
true. Statistical testing is all about the probability that the
estimate you found could have been drawn "by accident" if the null
hypothesis is true. When a statistic is unlikely to have been the
result of such an "accident" we call it significant. In small samples
you could more easily be "unlucky" and draw a "weird" sample with very
different coefficients than the population. Such accidents are a lot
less likely when you draw large samples than small samples, so in
large samples we should get more significant results.

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany


http://www.maartenbuis.nl
--------------------------
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