Why did I find this after my textbook was published? Damn it. I have a whole section about how Y-axis manipulation can make small differences look huge and then...I find this. Damn it. Source:   https://www.reddit.com/r/dataisugly/comments/1hjr01o/height_of_female_popstars/

 My favorite examples of bad data visualizations are the ones that use accurate data that was actually collected through seemingly ethical means but totally malign the data. The numbers are correct, the data viz is...not very truthy ( I'm looking at you, Florida. ) Especially when you mess up the data viz in a way that appears to be deliberate AND doesn't really strengthen your point. I'm also looking at you, The White House. Here is a story of a deliberate but pointless massaging of a y-axis. A story in Three Tweets. 1. The Biden Administration is doing a good job of encouraging economic growth, right? Take a gander at this bar graph. 2021 was a success...just look at the chart.  2. BUT WAIT. What's this? That y-axis is shady. I...just can't think of any software/glitch that could make this mistake by accident. ALSO: If you like Twitter, follow Graph Crimes.  3. The White House issues a correction featuring a pretty good data put, I would say.  FIN

I've shared these on my Twitter feed, and in a previous blog post dedicated to stats funnies. However,  I decided it would be useful to have a dedicated, occasionally updated blog post devoted to Twitter Statistics Comedy Gold. How to use in class? If your students get the joke, they get a stats concept. *Aside: I know I could have embedded these Tweets, but I decided to make my life easier by using screenshots. How NOT to write a response option.  Real life inter-rater reliability Scale Development Alright, technically not Twitter, but I am thrilled to make an exception for this clever, clever costume: This whole thread is awesome...https://twitter.com/EmpiricalDave/status/1067941351478710272 Randomness is tricky! And not random! ...