Claudia
You make reasonable arguments. It's debatable, ultimately, as the decision 
criteria can be thought of flexibly (as in, this is early so I used a softer 
criterion of .07, or similar arguments) OR as a disciplinary cut-off (as in, we 
use .05 in the social sciences based on reasoned consequences of Type I and 
Type II errors). To be honest, I find people often teach the later and do the 
former depending on a variety of factors. At any rate, I'm siding with the 
fingernail on a chalkboard metaphor, or, "NO, you are not being too picky", 
when it comes to the term that started this discussion. I distinctly remember 
an episode of MASH (episode 14 of season 1?) where Radar is attempting to 
impress a rather intelligent nurse and Hawkeye teaches him to say, "That's 
highly significant!" when he doesn't understand a point she's made. At least 
now it makes me laugh instead of cringing when someone says that. :)
Best,
Tim Shearon

_______________________________
Timothy O. Shearon, PhD
Professor, Department of Psychology
The College of Idaho
Caldwell, ID 83605
email: tshea...@collegeofidaho.edu

teaching: intro to neuropsychology; psychopharmacology; general; history and 
systems

"You can't teach an old dogma new tricks." Dorothy Parker



From: Claudia Stanny [mailto:csta...@uwf.edu]
Sent: Monday, April 22, 2013 12:27 PM
To: Teaching in the Psychological Sciences (TIPS)
Subject: Re: [tips] Polling...







"Highly significant" conflates statistical rarity with impact (importance of 
the effect, the size of the effect).

On the other hand, I think "approaching significance" can be useful and I will 
defend that practice (although I wouldn't push its use in a publication).

Many statisticians note the arbitrariness of the decision criterion (the 
magical .05) and argue that a result that would occur randomly with a 
probability of .051 or .052 or .06 (I could go on . . . it is a slippery slope) 
deserves closer examination than just deciding that the result is does not meet 
the criterion to be declared statistically reliable.  This rigidness in the 
decision process seems to reinforce the too-common treatment of statistical 
analysis as a ritual of taking out data (our sacrificial goat, as it were) to 
the oracle for a decision.  We can be more thoughtful than this.  (Abelson's 
excellent book, Statistics as Principled Argument, has some discussion of the 
thoughtful use of inferential statistics.)

Failure to reach the criterion can occur for reasons other than absence of an 
effect.  The near misses are worth examining.  Similarly, the just-made-it 
"successes" deserve replication and questions about Type I Errors.

Claudia




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