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. 

I know it is January 2025, but I want to write about something that happened during the Spring of 2024. I think it is a story about how it is never too late to do the right thing, making it great thing to think about here at the New Year. Data can't undo the past, but the way we manage it moving forward can provide the opportunity for some measure of equity. Back in May, professional baseball decided to include Negro League (NL), which existed from 2910 to 1948, baseball stats as part of Major League Baseball (MLB) stats. This is was done to allow for proper recognition of talented ML players. This changed some storied records for the league: https://www.mlb.com/news/stats-leaderboard-changes-negro-leagues-mlb This was a lot more than merging a couple of spreadsheets. As such, this story also serves as a lesson in data management and making desperate datasets the same. One that is a lot more moving than your typical story of data-cleaning. The following screenshots are from:  ...

This semester (SP 15), I taught an Honors section of Psychological Statistics for the first time. In this class, I decided to take my students to a minor league baseball game ( The Erie Seawolves , the Detroit Tiger's AA affiliate) in order to teach my students a bit about 1) applied statistics and data collection as well as 2) selecting the proper operationalized variable when answering a research question. Students prepared for the game day activity via a homework assignment they completed prior to the game. For this assignment, students learned about a few basic baseball statistics (batting average (AVG), slugging (SLG), and on-base plus slugging (OPS)). They looked up these statistics for a random Seawolves' player (based on 2014 data) and learned out to interpret these data points. They also read an opinion piece on why batting averages are not the most informative piece of data when trying to determine the merit of a given player. The opinion piece tied this exe...