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 --- You are currently subscribed to tips as: arch...@jab.org. To unsubscribe click here: http://fsulist.frostburg.edu/u?id=13090.68da6e6e5325aa33287ff385b70df5d5&n=T&l=tips&o=25144 or send a blank email to leave-25144-13090.68da6e6e5325aa33287ff385b70df...@fsulist.frostburg.edu
