I'm a fan of former NASA engineer and current YouTuber/science comm pro  Mark Rober . He meets the sweet spot of containing YouTube content that is safe for kids but also engaging for adults. You may know him for creating obstacle courses for squirrels in his backyard and holding the world record for the tallest elephant toothpaste explosion .  Recently, I discovered that he made a stats-adjacent video  explaining machine learning by studying baseball signals and creating a way to de-code baseball signals . Anyway, if you touch on your topics in your classes, this is a great, quick explainer. It is well-edited, well-produced, and has captioning. You don't need to be a baseball fan to follow this example. 

 I have written about the NFL's response to concussion data as a case study of how to obfuscate data. This has been covered in many places, including in The Atlantic and on PBS . In my experience, concussions are a prime source of conversation for traditionally college-aged students. Many of them were high school athletes. Fewer are college athletes. Most college students have personally experienced a concussion or loves someone who has. Now, the University of Pittsburgh is opening the National Sports Brain Bank . This is for athletes, not just football players. Two former Steelers have promised their brains, as have two scientists who played contact sports.  Here is a press release from the University of Pittsburgh . Here is a news report  featuring the two Steelers who have promised to donate their brains. However, as described by Aschwander, we still don't know how many football players have CTE (please read this piece, it is such good stats literacy from Aschwander...

Looking to freshen up your readings for Research Methods? Or for a good, brief RM primer for a stats or psych class? Check out Our World in Data's "Why randomized control trials matter and the procedures that strengthen them" . Added bonus: Our World in Data dived into their data archives to illustrate each piece with their own research. I don't know about you, but my brain far prefers abstract concepts paired with concrete examples.  Some of the classic include: -Why we need RCT. https://ourworldindata.org/randomized-controlled-trials#what-are-randomized-controlled-trials -Why causal inference is hard. https://ourworldindata.org/randomized-controlled-trials#the-fundamental-problem-of-causal-inference -Why we need control groups. https://ourworldindata.org/randomized-controlled-trials#the-control-group-gives-us-a-comparison-to-see-what-would-have-happened-otherwise