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.

*Above*

*below*

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.