I think that interactive visualizations are better than lengthy, wordy text books when it comes to illustrating statistical principles. One little GIF or interactive website can do a far better job than text or words. For example: Everything that Kristoffer Magnusson has given us (effect sizes, correlations, etc.). Here is a new tool for explaining critical regions in Intro Stats. Matthew  McBee created an interactive in shinyapps that shows how critical regions change a) depending on test, b) sample size, change of the shape of the distribution. With the ol' t-test, you can show how the critical values move around with degrees of freedom What your t-test critical values looks like at df = 3... ...versus how the those critical values look at df = 80 Also, you can do the same thing but with F curves. Andplusalso: Matt has also created shiny apps to adjust p-values for multiple comparisons , AND another one for calculating p-values based on a test statistics ...

There are a lot of lessons in this one study  (Craig, Nelson, & Dixson, 2019): Within subject design, factorial ANOVA and interactions,and data is available via OSF. Let's begin: TL: DR: The original study looked and the presence or absence of beards and whether or not this affected participants' ability to decode the emotional expression on a man's face. Or, more eloquently: TL: DR: Their stimuli were pictures of the same dudes with and without beards. And those weren't just any dudes, they had been trained in the Ekman facial coding system as to make distinct expressions. Or... One participant, rating the same man in Bearded vs. Non-bearded condition, provides a clear example of within subject research design. This article also provides examples of interactions and two-way ANOVA. Here look at aggression ratings for expressing (happy v. angry) and face hairiness (clean-shaven v. beard). Look at that bearded face interaction! Bearded guy...

Eric Roston and Blacki Migliozzi put together some great data visualizations illustrating different factors that may or may not contribute to global climate change ( "What's Really Warming the World?" ). I couldn't capture it in this blog post, but the data is animated and interactive as to highlight change over time. Very slick. This got me thinking about multiple regression, which studies different variables (X 1 , X 2... ) that may or may not contribute to some outcome (Y), and how we can use this website as a conceptual example of multiple regression. Here, the graph features multiple "predictor"/X 1 , X 2 , X 3 , X 4 variables (greenhouse gasses, ozone, land use, aerosols) as well as the predicted/Y variable (global temperature). we can see the aerosols are likely a very poor predictor while greenhouse gasses are likely a good predictor. This page can also be used to explain plain old linear regression. This example compares one predictor/X...

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: https://xkcd.com/833/ To explain sufficient sample size in research: https://xkcd.com/507/ To explain good statistics manners/how to appropriately ask for stats help: https://m.xkcd.com/2116/ To explain error bars: https://xkcd.com/2110/ T-test and the t-curve: https://xkcd.com/2110/ Linear relationships: https://xkcd.com/605/ The Normal Curve: https://xkcd.com/2118/ Cherry picking, p-...

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 ...

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: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0132382           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 lif...

Y'all. I love collecting examples of awesome cookies and cakes and cupcakes beautified with statistics and data and graphs. Here is my Pinterest Board. My goal is to SOMEDAY have the time and skills to make some of my own. Until then, I will gush over other peoples accomplishments: