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.
Ryosuke Fujii's (2026) How to Design Effective Scientific Figures lives up to its title and is written at a level accessible to undergraduate and graduate students when conference time rolls around. It was published in Nature, and it focuses on understanding which type of figure to use depending on a) your data and b) your audience. It is only three pages long, and I found myself nodding and agreeing with the main points, summarized here: Additionally, the article contains this helpful flowchart detailing when to use which graphs. I appreciate that the flowchart isn't jargon-heavy and that it focuses on functionality.