This hilarious quip made me think of regression.   So I created a wee data set ( available here ): It features this scatter plot of the data (r = .99). It also includes JASP output of the regression for this data (a person born in 2020 is predicted to marry Dennis Quaid in 2052). 

I love court cases that hinge on statistics, like these two US Supreme Court cases: Hall vs. Florida , Brown vs. Entertainment Merchants Association . Such examples demonstrate the relevance of what students are learning in our class: in Hall vs. Florida, the margin of error saved a criminal from the death penalty. The majority opinion in Brown vs. Entertainment Merchants Association reiterates that correlation does not equal causation and brings up effect sizes. A recent case in Florida demonstrated that research about voting and candidate order on ballots can unfairly advantage candidates at the top of the list. Here is a brief summary from the Miami Herald : https://www.miamiherald.com/news/politics-government/state-politics/article237417779.html Here are portions of the actual decision from the Election Law Blog . The highlight in the paragraph below in mine, since the primacy effect is also something we talk about in Intro Psych. Also, note the terrific footnote....

This is for my psychometric instructors. (Glorious, beloved) Pew Research Center compared participant behavior when they have to answer the same question in either a) forced-choice or b) check-all format. Here are the links to the short report and to the long report . What did they find? Response options matter, such that more participants agreed with statements when they were in the forced-choice format. See below: So, this is interesting for an RM class. I also like that the short report explained the two different kinds of question responses. The article also explores a variety of reasons for these findings, as well as other biases that participants exhibit when responding to questionnaires: