As Gerd Gigerenzer once said: we need more statistical thinking and fewer 
statistical rituals.
Chris
.......
Christopher D Green
Department of Psychology
York University
Toronto, ON M6C 1G4

[email protected]
http://www.yorku.ca/christo

> On Feb 12, 2014, at 7:30 PM, John Kulig <[email protected]> wrote:
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> 
> 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 
> ==========================
> 
> From: "Jim Clark" <[email protected]>
> To: "Teaching in the Psychological Sciences (TIPS)" 
> <[email protected]>
> Sent: Wednesday, February 12, 2014 6:46:59 PM
> Subject: RE: [tips] Scientific method: Statistical errors : Nature News & 
> Comment
> 
> Hi
> 
> Interesting article, although I need to think more about it.  One obvious 
> weakness is the old canard about effect size being a better indicator of 
> importance than p value.  The author uses the example of a divorce rate 
> change being tiny: "meeting online nudged the divorce rate from 7.67% down to 
> 5.96%."  One source indicates that there are about 2,000,000 marriages in the 
> USA per year. 7.67% is 153,400 divorces, 5.96% is 119,200 divorces, for a 
> reduction of 34,200 divorces or 22.3% fewer divorces every year.  Not exactly 
> what I would call a "tiny" difference.
> 
> Take care
> Jim
> 
> Jim Clark
> Professor & Chair of Psychology
> 204-786-9757
> 4L41A
> 
> 
> -----Original Message-----
> From: Christopher Green [mailto:[email protected]] 
> Sent: Wednesday, February 12, 2014 4:43 PM
> To: Teaching in the Psychological Sciences (TIPS)
> Subject: [tips] Scientific method: Statistical errors : Nature News & Comment
> 
> An interesting article about the problems of p-values that might even be 
> understandable to undergraduates. 
> http://www.nature.com/news/scientific-method-statistical-errors-1.14700
> 
> Chris
> .......
> Christopher D Green
> Department of Psychology
> York University
> Toronto, ON M6C 1G4
> 
> [email protected]
> http://www.yorku.ca/christo
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