Alright, this is a 30-second long example for a) bimodal distributions and b) why measures of variability matter when we are trying to understand a mean. And that mean is...AGE OF DEATH. My inspiration for this tweet is: I’m just a girl, standing in front of the internet, asking it to understand that historical life expectancies doesn’t mean most people died at 45 but rather that infant mortality was super high and pulled down the average. — Angelle Haney Gullett (@CityofAngelle) January 12, 2022 Gullett refers here to the commonly held belief that if the mean life span Back In The Day was 45, or thereabout, everyone was dying around 45. NOT SO. Why? The short answer is no. Broadly speaking, there were two choke point of human mortality. Younger than 5, and again around 50. If you made it through those, barring accidents, you likely had what was a normal lifespan of ~65-70 years. And this is why I’m no fun at parties 😂 — Angelle Haney Gullett (@CityofAngelle) January 12, 2022 OK. An...

Many thanks to my colleague, Andrew Caswell, for sharing this Reddit post with me: https://www.reddit.com/r/dataisbeautiful/comments/s75sm7/oc_us_life_expectancy_vs_of_taco_bell_locations/ So, this alone is an excellent example of correlation and the third variable problem. But...more delightfully, the Redditor who created this graph also shared where he found this data (https://www.nicerx.com/fast-food-capitals/, https://worldpopulationreview.com/state-rankings/life-expectancy-by-state). BETTER STILL: I downloaded and organized all of the fast-food data and mortality data and put it in one spreadsheet for you all. Do All The Correlations! Teach your students about Bonferroni corrections! Figure out the fast-food restaurant that correlates the most strongly with mortality!   PS: Did you know that there is an option to download data from a website in Excel?  The fast-food data was presented in an embedded, scrolly table, and that Excel option made it easy-peasy to do...

One of the nine stages of reading a scientific paper.   Ahahaha. This article by Adam Ruben , writing for Science, makes fun of how difficult it is to read scientific papers. I think your students will enjoy it, especially halfway through their senior thesis/project. It pairs nicely with more traditional guides on reading scientific papers, like the classic Jordan and Zanna piece or this more recent blog post from Dr. Jennifer Raff . It captures some real gems. Peer review, while imperfect, still scrutinizes research papers like no other form of publication: It also contains some niche humor your students may not appreciate, but I do.

So, the Journal of the American Medical Academy publishes v isual abstracts  for some of its research articles. I've written about them before (in particular, this example that illustrates an ANOVA ). These abstracts succinctly summarize the research. They feel like an infographic but contain all of the main sections of a research paper. They are great. They quickly relate the most essential parts of a research study and have a home in Intro Stats.  I love them in Psych Stats and use them for several reasons. 1. Using medical examples reminds Psych Stats students that Psych Stats is really Stats Stats, and stats are used everywhere. 2. These are simplified real-world examples. JAMA creates these to help highlight essential facts for journalists and the public, so Intro Stats students are more than ready to take these on. 3. I like to use these as a quick review of some of the inferential tests we teach in stats. This is no guarantee that basic stats were used in the project, b...