eCOTS 2012 - Virtual Poster #3

"On teaching statistical inference: What do p values (not) mean?"
with Bruce Blaine, St. John Fisher College

Hosted by: Leigh Slauson, Capital University

The misunderstanding and misuse of null hypothesis significance testing (NHST) for statistical inference is well documented, particularly among behavioral science students and researchers (Oakes, 1986). Central to this problem is the misinterpretation of p levels generated by significance tests. Two common errors made are: interpreting p as an effect size estimate, and interpreting p as the probability of the null hypothesis being true or false. Each misinterpretation can be exposed and examined with classroom data analytic exercises. The first involves showing students that increasing sample size reduces p levels but does not change effect size estimates. The second involves showing that a meta-analysis of "nonsignificant" studies produces a significant meta effect. These exercises serve a larger pedagogical goal of promoting estimation, as opposed to or along with, NHST methods for statistical inference.

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Liz Prather:

Thank you.

Bruce Blaine:

You're welcome. Bruce

Lynn Fisher:

I particulary liked your remedy for the "significance" fallicy, thank you.

Bruce Blaine:

I am glad that you found it helpful. I find, when I teach psych stats, that students are often very surprise--even a bit resistant to accepting--that "nonsignificant" studies can provide data that actually supports H1.
I also like it because it allows me to insert some simple meta-analysis into the course!

Dolores Frias-Navarro:

Congratulations on your presentation. Students really have great difficulty understanding the statistical decision process. We have investigated for years about the use and abuse tests of statistical significance als (Monterde-i-Bort, H., Frías-Navarro, D., & Pascual-Llobell, J. (2010). Uses and abuses of statistical significance tests and other statistical resources: a comparative study. European Journal of Psychology of Education, 25(4), 429-447). Currently working hard the analogy between the process of trial and the process of statistical hypothesis testing.We used a movie as an analogy
Dolores Frias-Navarro
University of Valencia (Spain)