You all. Many people, but mostly Jenny Terry and Andy Fields, but also a number of my Twitter mutuals,  collected a crap ton of data from statistics students worldwide .  See: Here is the article describing the project . The data is embargoed until October 2024, but you can contact the corresponding authors if you would like early access. Also, they have tons and tons of documentation available at OSF . So you can come up with your own hypotheses and test them. Which is very, very generous.

  The unstoppable pop of Taylor Swift (reuters.com) Here are some ideas for using this to teach statistics: Data visualizations and visualization guides: With cats, y'all. And the Taylor Swift handwriting font. I love the whole vibe of this as well as how they explain their data visualizations. Operationalizing things: The page describes three Spotify metrics for music: Acousticness, danceability, and emotion. The data visualization contains a numeric value for each metric and a description of the metric's meaning. DATA!: Okay. This is an excellent example of things already. And it is delightful. Then I thought, "Oh, wouldn't it be fun if this was in spreadsheet form!" (I think that A LOT, friends). But, as I write a book and my syllabi, I don't have time for that,  BUT A REDDITOR DID HAVE TIME FOR THAT . Dr. Doon created a spreadsheet with 18 columns of Spotify data for each son. It doesn't include the Midnights data but is still a fantastic amount of dat...

Consumer Affairs released a list of America's best and worst drivers . It is a short article but contains many good stats nuggets. 1. Ratio and ordinal versions of the same data. 2. Where did the ratio data come from? Take a look at the Methodology. 3.  Here is the data for the twenty most terrible driver s. It includes the nominal/ratio data I shared above and the top four bullet points from the image above. 4. Where did they find their data? Lucky for us, they cite their data. Which is good form, right? But also, it is an example of how much hecking data is out there.