This YouGov data asked Americans to rate how much they enjoy a variety of summer activities. They graphed out the percentage of people, divided by demographics, who indicated that they like or love a summer activity. One of the demographics they used was age. Which makes me feel seen, and I can already imagine how I will poke fun at myself, a 46-year-old who hates outdoor sports. More  pedagogically, I can use this data when introducing paired  t -tests. Specifically, I can get them to ponder this data and think about why  the age differences exist.   Here is the data visualization for activities where there is a big age gap in enjoyment: Here is the data visualization for activities where there is not a big age difference: I think they really missed out by not including birdwatching on this list. I'm 46 and I hecking love it.  I could also see this as an example in a Developmental or Psychology of Aging course. What is driving the differences between older...

This summer, the Psi Chi Journal of Psychology Research published  Rouse, Russel, and Campbell's Beyond the textbook: Psi Chi Journal articles in introductory psychology courses. It is a curated list of paywall-free Psi Chi articles, mostly with student co-authors, that are peer-reviewed and of an appropriate writing level and length to use in an Introduction to Psychology course. The authors provide the following information for each of the articles: In addition to being appropriate for Into Psych, these articles are also perfect for Intro Stats. In my classes, I emphasize the ability to read and write simple result sections. One way I would review this skill is by showing my students Results sections from published research and asking them to identify the test statistics, effect size, and other relevant information. This selection of articles features clear and concise results sections for t -tests, ANOVA, factorial ANOVA, regression, and correlation. I created a spreadsheet...

 I'm surprised I haven't shared this in this space already. It is one of my favorite data points ever. Clearly, I have favorite data points. https://www.economist.com/graphic-detail/2019/07/04/are-extraterrestrials-extra-patriotic How to use in class? 1) There is data for EVERYTHING if you look hard enough, 2) WHY might this relationship exist (heat stroke, staring at the sky, drinking, freedom, fireworks)?  If you like this example, check out my W.W. Norton & Co. textbook,  Psychological Statistics for Everyone . 

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 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 animated...

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.