Any discipline always has subsets of argument, typically about definitions, methodologies, process or significance.  Statistics, of course, is no different.  Below is an interesting article from the Washington Monthly about what constitutes statistical significance.  The article is OK, but the commentary below it even better.  See http://www.blogware.com/admin/index.cgi/cmd=post_article

LIES, DAMN LIES, AND….Via Kieran Healy, here's something way off the beaten path: a new paper by Alan Gerber and Neil Malhotra titled “Can political science literatures be believed? A study of publication bias in the APSR and the AJPS.”
It is, at first glance, just what it says it is: a study of publication
bias, the tendency of academic journals to publish studies that find
positive results but not to publish studies that fail to find results.
The reason this is a problem is that it makes positive results look
more positive than they really are. If two researchers do a study, and
one finds a significant result (say, tall people earn more money than
short people) while the other finds nothing, seeing both studies will
make you skeptical of the first paper's result. But if the only paper
you see is the first one, you'll probably think there's something to it.


The chart on the right shows G&M's basic result. In statistics
jargon, a significant result is anything with a “z-score” higher than
1.96, and if journals accepted articles based solely on the quality of
the work, with no regard to z-scores, you'd expect the z-score of
studies to resemble a bell curve. But that's not what Gerber and
Malhotra found. Abovebelow
a z-score of 1.96 there are far fewer studies than you'd expect.
Apparently, studies that fail to show significant results have a hard
time getting published.