I use this same research as an example of how proportion of variance 
effect sizes can be deceptive.  Look at the ratios of odds or risks, or the 
binominal effect size display, and the picture is very different.

Cheers,
[Karl L. Wuensch]<http://core.ecu.edu/psyc/wuenschk/klw.htm>
From: John Kulig [mailto:[email protected]]
Sent: Wednesday, February 12, 2014 7:30 PM
To: Teaching in the Psychological Sciences (TIPS)
Subject: Re: [tips] Scientific method: Statistical errors : Nature News & 
Comment


Thanks Chris .. this is a terrific article and many undergrads can plow through 
it. I have gone back and forth on the p versus CI (which is simply rearranging 
the math) versus effect size issue and have come to the conclusion that we have 
to keep our options open and not use one rule to evaluate research findings. In 
my stat class - after doing my lecture on how an IQ difference of 1 point can 
be significantly different when N = 5000 per group - I sometimes talk about the 
1988 (?) study of aspirin and Myocardial infarction in JAMA or NEJM (I am home 
away from my notes) which found a .8% reduction in MI from a sample of 11,000 
placebo controls (risk = 1.7%) and about 11,000 who took aspirin (risk = .9%). 
The chi square is p < .001 but the effect size is tiny, but even that 1% drop 
is important when the stakes are high and you are one of the roughly 100 who 
was spared a MI. that's when I introduce "relative risk" thinking: .9 versus 
1.7 means the chance of a MI is cut in half. That type of comparison is 
especially important when dealing with low base rate diseases. And thanks Jim 
for the divorce example ...

JK

==========================
John W. Kulig, Ph.D.
Professor of Psychology
Coordinator, Psychology Honors
Plymouth State University
Plymouth NH 03264
==========================

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