First, a pun: American athlete Athing Mu broke the American record for the 800m. I guess you could say...that Mu is anything but average!! HAHAAAHAHAHHA. https://twitter.com/Notawful/status/1409456926497423363 Anyway. It is late June 2021, and my Twitter feed is filled with amazing athletes qualifying for the Olympics. Athletes like Sydney McLaughlin. That picture was taken after McLaughlin a) qualified for the 2021 Olympics AND b) broke the 400m hurdle world record. Which is amazing.  Now, here is where I think we could explain effect size interpretation. How big was McLaughlin's lead over the previous record? From SpectrumNews1 McLaughlin broke the world record by less than a second. But she broke the world record so less than a second is a huge deal. Similarly, we may have Cohen's small-medium-large recommendations when interpreting effect sizes, but we always need to interpret an effect size within context. Does a small effect size finding explain more variance than any pre...

It's coming, guys. But let's get ahead of it. I thought I would re-share some resources that you may want to consider working into your curriculum this year. I picked out a few lessons and ideas that also require a bit of forethought and planning, especially if they become assessment measures for your class. Center for Open Science workshops: As previously discussed on this blog , COS offers f ree consultation  (face-to-face or online) to faculty and students in order to teach us about the open framework for science. They provide guidance about more more traditional statistical issues, like power calculations and conducting meta-analysis in addition to lessons tailored to introducing researchers to the COS framework. Take your students to an athletic event , talk about statistics and sports : I took my students to a baseball game and worked some statsy magic. You can do it, too. If not a trip to the ballpark, an on-campus or televised athletic event will w...

I use this video clip to introduce probability, especially since I focus on the Equal Likelihood Outcome model. Also, I work at a liberal arts university, so all of my students have had Introduction to Philosophy by the time they take my statistics class (which can make this video funny in a different way).

Seems appropriate. I like football, but I LOVE data. For a better look in case you don't have bionic eyes or a magnifying glass next to your screen, check out CNN for the original graphics.  Given the trends within the points spread, it looks like the games are becoming more competitive over time. And the linebackers are getting scarier over time.

This is a site I found this summer during the hype surrounding the London Summer Olympics. If you enter your weight and height into the site, it will match you with the Olympian who has the most similar weight and height as to predict your ideal Olympic sport. Needless to say, there are more than two factors that determine one's ideal sport. Which is a great starting point when discussing multiple regression and making predictions. Students can discuss whether or not they've ever played the sport predicted (what is handball? I dunno) as well as list other factors that determine athletic preferences (SES, individualistic vs. collectivist tendencies, body composition, hand eye coordination, etc.).