Saturday, March 9, 2019

xkcd comics and statistical thinking.

Xkcd is a gift to Statisitcs instructors. Author Randall Monroe shares his humor and statistics knowledge. I think that many of his comics can be used as extra credit points, in that you don't get the joke unless you get the conceptual statistical knowledge behind the joke.

NOTE: I have included images here, but you really, really should go to the original comics and cursor over for the messages to view the alternative text.

NOTE TWO: This is not a comprehensive list but I will try to update it as Monroe shares more comics.

To teach APA formatting:
To explain sufficient sample size in research:

To explain good statistics manners/how to appropriately ask for stats help:

To explain error bars:

T-test and the t-curve:
Linear relationships:

The Normal Curve:

Cherry picking, p-hacking, and other statistical ills:

Line fitting:

Tuesday, March 5, 2019

Elizabeth Page-Gould's PSY305: Treatment of Psychological Data

Two things this week: 1) Open Science Framework can be used to share teaching materials and 2) Dr. Page-Gould shows us how to do just that, and how to do it very well.

Most people who would visit this blog have heard of the Open Science Framework. You probably know that it is a popular place to share research projects/data/pre-register your jam/share materials, but did you know that it is also a popular place to share teaching resources?

Dr. Page-Gould recently shared her whole stinking upper level Stats/RM class, Treatment of Psychological data. And it is beautiful and good and makes me feel like an entirely inadequate statistics instructor.

Like...she shared EVERYTHING and it is beautiful and a great example of how to fold the "new statistics" into undergraduate stats. Lectures, example data, and lab resources (and rubrics for grading her labs) are available. This is an upper level course but it covers topics that should be included in Introduction to Statistics. I will be stealing some PPTs for new ideas on explaining Bayes Theorem (lecture 9), effect size (lecture 3).

Monday, February 25, 2019

Likelihood of Null Effects of large

This example provides evidence of data funny business beyond psychology, shows why pre-registration is a good thing, AND uses a chi-square. Bonus points for being couched in medicine and prominently featuring randomized controlled trials (RCT).

Basically, Kaplan and Irving's research checked out the results for RCTs funded with grants from the National Heart, Lung and Blood Institute. See below for how they selected their studies:

And what did they find? When folks started registering their outcomes, folks started to get fewer "beneficial" results. Which probably REALLY means that some of those previous "beneficial" results were not so beneficial, or the result of some data massaging. See below:
Another reason to love this example: It is a real life chi-square that is easy to understand! I feel like I don't have enough great chi-square examples in my life. Or maybe I just can never have enough? Anyway, see below for their Results:

For all of my coverage related to replication, see: