This excellent interactive tool provides good examples  of chi-square thinking and interactions.  For more commentary, this  write-up of Orth's researc h  is an easy read. Also, it is an example that doesn't involve politics or COVID. There is a time and place for both in the statistics classroom, but I've missed good, accessible data that doesn't have ANYTHING to do with divisive issues.  The data is from research that asked people how they watch TV: Streaming services, cable TV, binge-watching, etc. All sorts of information on how we watch TV, and with this easy-to-use website, you can create tables from this data. Tables that help you teach all kinds of ideas. You can turn chi-square goodness of fit for whether or not people binge their TV. Into a chi-square test of independence by investigating binging by age (categorized).  You can also change the data visualization so you can see the data in a very traditional chi-square table You can also use these c...

 I found this Tweet, which leads to a brief report on BBC. A recent report from the World Obesity Federation shows COVID death rates are higher in countries where more than half the population is overweight. Cause and effect, or bad statistics? @TimHarford and @d_spiegel explore - with some maths from me. You can listen on @BBCSounds https://t.co/hevepmz8RC — stuart mcdonald (@ActuaryByDay) March 14, 2021 The BBC has a show called "More or Less," and they explained a recent research finding connecting obesity to COVID 19 deaths.  Here is the original research study . Here is a pop treatment of the original study . For more stats news, you can follow  "More or Less" on Twitter . And they cram, like, a half dozen lessons in this story. It is amazing. I've tried to highlight some of the topics touched upon in this story. How can you use it in class? I think it would be a good final exam question. You could have your students listen to the story, and highlight ...

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...

I've shared these on my Twitter feed, and in a previous blog post dedicated to stats funnies. However,  I decided it would be useful to have a dedicated, occasionally updated blog post devoted to Twitter Statistics Comedy Gold. How to use in class? If your students get the joke, they get a stats concept. *Aside: I know I could have embedded these Tweets, but I decided to make my life easier by using screenshots. How NOT to write a response option.  Real life inter-rater reliability Scale Development Alright, technically not Twitter, but I am thrilled to make an exception for this clever, clever costume: This whole thread is awesome...https://twitter.com/EmpiricalDave/status/1067941351478710272 Randomness is tricky! And not random! ...