This one is for all of my psychometric instructors. CNN created an engaging, interactive website to explain rank choice voting using ice cream flavor preference.  It was created due to the 2025 NYC mayoral primaries, but uses ice cream instead of humans to make for a good explainer that may have a home in your classroom. https://www.cnn.com/interactive/2025/06/politics/ranked-choice-voting-explained-dg/ First, you rank order your top five favorite ice cream flavors out of a field of ten. Then, you can view all users' ranking data, and see how the distribution changes when the least popular flavor, Rocky Road, is eliminated and the rocky road voters' votes are redistributed. The vote relocation goes on and on... Finally, you get to see the winner, chocolate. Rank choice voting is one of those concepts that is way, way easier to explain with a bit of animation and a very simple premise. I couldn't capture it in my screenshots, but the flavor elimination and redistribution are...

While most of my class time is dedicated to the specifics of performing and interpreting inferential tests, basic statistical literacy and thinking are equally important lessons. Here are some of the big-picture literacy ideas I want my students to think about in my stats classes: 1. How can we use data to understand patterns to make predictions? 2. How can we separate the signal from the noise?  3. How can data actually inform real life and current events? 4. How can we repurpose existing data in a world where data is everywhere? Here is an example I JUST found that addresses all of these ideas. The  Pentagon Pizza Report is an X account that monitors Google "Popular times" data in pizzerias near the Pentagon to predict military activity.  The X account asserts that unusually high, later-than-normal foot traffic at pizzerias near the Pentagon (x) may indicate that Pentagon military staff are working late and need to grab take-out for dinner(y).  Most recently, the...

I've been writing this blog, statistics pedagogy articles, chapters, and a whole statistics textbook for over ten years. I'm at the point where I see silly stuff on the internet, and it automatically translates to a statistics example. Like this recent Tweet from Sheetz about the Pirates/Philly series this weekend. https://x.com/sheetz/status/1923397811778785489 This is an unapologetically Western PA tweet. I will be using it as a chi-square goodness-of-fit example with my Western PA students at Gannon University this Fall. I even created a data file that mimics the findings (Methods:  n  = 380, Results: p < .001. Conclusion: Sheetz followers on Twitter love some curly fry). If you are a poor, unfortunate soul who has never enjoyed treatz from Sheetz, I feel bad for you. Look up your favorite regional brands on Twitter and translate one of their polls into a chi-square example. Or travel to your nearest Sheetz to experience some damn joy. 

State Farm, a prominent American insurance provider, shared data that ranked American states based on the number of animal-related (mostly deer) car accident claims filed per state .  I blogged about this data previously , and I am returning to it now as part of my semi-regular Discussion Board Ideas series on this blog. I have been using this prompt in my online stats class in NW PA for about a year now. I'm going to share some of that success here. Note: PA is #4 for deer-related car accident claims, so this data resonates with my students.  I use this for the fifth of seven weeks in my online class, so the students are comfortable with the class format and one another by then. Here is the exact prompt I use: I have a weird question for you: How do you think Pennsylvania ranks when it comes to the number of car accident insurance claims involving colliding with animals? Yes, I am on my soapbox about safe night-time driving in PA. Once you have your guess, check against...

A clever YouTuber, PWA , built a channel with nearly a million followers based on animated videos that compare nations based on data. Every nation is a sassy sphere. Each grows and shrinks in size, in comparison with other nations, as the data is presented. Like this image, illustrating national debt as a portion of GDP... I swear, it is funny and engaging without trying too hard. Also, for better or worse, framing data sharing and visualization as a thing that can make you a successful influencer WILL grab your students' attention. I think these videos would make good  bell-ringer s ( TM Janet Peters) for the start of your class. This influencer makes a ton of videos, and they aren't all related to data, FYI. Here are a few good examples for Stats class:  National Debt: In this clip, the countries compare their national debt. This video discusses some of the choices statisticians make with their data. For example, they compare their national debt in USD and then compare...

Look at how Dima Yarovinsky turned the Terms and Conditions documents for several social media platforms into foreboding and beautiful art/bar graphs illustrating how much we sign away without reading. Note: He even uses the X axis to describe the length of and reading time for each T&C statement!  I think data is beautiful. This example does a good job of showing the beauty and impact of good data visualizations to my students. This isn't a huge example to use in class, but I will use it next time I discuss bar graphs. For more from the artist, in his own words, visit his webpage .  For a thought review of this art, see this article by Emma Taggart .

Does anyone else teach correlation and regression together at the end of the semester? Here is a treat for you: Updated data on Leonardo DiCaprio, his age, and his romantic partner's age when they started dating. A few years ago, there was a dust-up when a clever Redditor r/TrustLittleBrother realized that DiCaprio had never dated anyone over 25. I blogged about this when it happened. But the old data was from 2022. Inspired by this sleuthing,  I created a wee data set, including up-to-date information on his current relationship with Vittoria Ceretti, so your students can suss out the patterns that exist in this data.