I came across a Reddit post  in which a user did a quick-and-dirty data collection of the ethnicities of the three top-billed actors in each of 100+ million USD-earning movies between 2022 and 2025. They then compared the data to US census data.    Regardless of how Reddit reacted, I saw this and decided that it would make a good example for explaining and performing a chi-square with expected proportions. I'm so fun at parties, guys. While the original sample was 228, I created an imitation sample ( n = 100) with the Hollywood data as the observed data. I used the US census demographic percentages as the expected proportions.  Here is my n = 100 imitation data, in JASP , .TXT , and a text file of the R code generated by JASP. AND PLUS ALSO: The OP in Reddit gave their quick-and-dirty research methodology for collecting data on the ethnic breakdown of the top-billed actors in very successful movies. I think you could challenge your RM students to consider how they ...

A research study counted how often ChatGPT made up citations for three different categories of mental disorders (binge eating, body dysmorphic, and major depressive). They used a chi-square to determine if rates of made up citations differed by disorder (they do).  If ever there was an article that belonged on this blog, this is it. You can use it in your stats class as an example of chi-square and/or as a warning to students if you ask them to perform literature reviews for your class. The original paper, Influence of topic familiarity and prompt specificity on citation fabrication in mental health research using large language models: Experimental Study was published in December 2025, and summarized by PsyPost  shortly after publishing.  What the researchers did: What the researchers found: How to use in class: 1. This is a good chi-square results section. They shared the test value and the p value, of course, but I like how they shared the varying rates of inaccuracy...

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. 

This excellent interactive tool provides good examples  of chi-square thinking and interactions.  For more commentary, this  write-up of Orth's researc h  is an easy read. Also, it is an example that doesn't involve politics or COVID. There is a time and place for both in the statistics classroom, but I've missed good, accessible data that doesn't have ANYTHING to do with divisive issues.  The data is from research that asked people how they watch TV: Streaming services, cable TV, binge-watching, etc. All sorts of information on how we watch TV, and with this easy-to-use website, you can create tables from this data. Tables that help you teach all kinds of ideas. You can turn chi-square goodness of fit for whether or not people binge their TV. Into a chi-square test of independence by investigating binging by age (categorized).  You can also change the data visualization so you can see the data in a very traditional chi-square table You can also use these c...