Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
And papers cited therein.
On Fri, May 13, 2011 at 10:45 AM, John Litfiba wrote:
> I would be definitively interested if by chance you have in mind a
> paper that discuss the large sample side effects on P-values that you
The argument is straightforward: with larger sample sizes we are able
to detect smaller and smaller effects. If we included a variable in
our model than it is extremely implausible that the effect of that
variable is exactly zero (i.e. the null hypothesis is true). If we
reject the null hypothesis is rejected that the effect was so small
that the dataset was not large enough to detect it. So by getting ever
larger samples we will start to find ever more effect, but they will
be so small that they are substantively irrelevant (even though they
are statistically "significant").
One discussion of this is:
Raftery, Adrian E. 1995. "Bayesian Model Selection in Social
Research." Sociological Methodology, 25: 111-163.
Also see the responses to this article that appeared in the same issue.
* For searches and help try: