1) Not every example needs to be big and fancy. 2) Sometimes you just need to refresh a lecture. By "refresh", I mean both new-to-you examples and examples that are, objectively, new. That is why I'm adding this example from the New York Times to my Fall 2026 classes. It was published in the summer of 2026, based on 2025 data that explores both the overall popularity of emojis AND generational differences in emoji usage. How to use in class: 1) A lesson in repurposing existing data sets: 2) Ordinal/rank order data: Here are the top ten emojis for 2025, as well as their change in rank. The rise of the "loudly crying face" is concerning! If a top ten list exists...then certainly a bottom 10 list exists. See: Please see below for emojis 490-500. Poor bread. You can also discuss rank data with this list of fastest rising emojis. Or you can use this example of emojis that are increasing in popularity, with a dash of relative percentages: 3) They also looked at emo...
IG users antiart et al. (2026) inadvertently created a great example of a non-significant correlation and/or regression for you to use this fall when they created a scatter plot that illustrates a celebrity's nepo baby (or old money) privilege vs. their career accomplishments. https://www.instagram.com/p/DXKWBlHCicc/ It is funny, right, but it does get to the guts of linear relationships. Some nepo babies are talented, like Miley Cyrus. Some have not (poor Chet Hanks). You also have folks who have done much despite humble beginnings, like Eminem and Obama. Hence, non-significance/non-predictive linear relationship. Also, sometimes I think stats teachers (myself included) lean too far towards only sharing statistically significant/medium-to-large effect sizes, but they need to see all types of results. Also, I can't identify half of these people, but I bet your students can.