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:

"(Algorithms) are used so heavily, they don't just predict the future, they are the future." -Cathy O'Neil ^This quote from this NPR story made me punch the air in my little Subaru after dropping my kid off to school. What a great sentence. There are many great one-liners in this little five-minute review of algorithms. This NPR story by Dina Temple-Raston is a great primer for All The Ethical Issues Related To Algorithms, accessible to non- or novice-statisticians. It clocks in at just under five minutes, perfect as a discussion prompt or quick introduction to the topic. How to use in class: They talk about regression without ever saying "regression": "Algorithms use past patterns of success to predict the future." So, regression, right? Fancy regression, but that one line can take this fancy talk of algorithms and make it more applicable to your students. Sometimes, I feel like I'm just waving my hands when I try to explain thi...

"I believe that we owe it to our children to prepare them for a world they will encounter, a world driven by data. Basic data fluency is a requirement, not just for most good jobs, but for navigating life more generally." -Steven Levitt Preach it, Steve. This edition of the Freakonomics podcast featured guest host Steven Levitt. He dedicated his episode to providing evidence for an overhaul of America's K-12 math curriculum. He argues that our kids need more information on data fluency. I'm not one to swoon over a podcast dedicated to math curriculums, but this one is about the history of how we teach math, the realities of the pressures our teachers face, and solutions. It is fascinating. You need to sit and listen to the whole thing, but here are some highlights: Our math curriculum was designed to help America fight the Space Race (yes, the one back in the 1960s). For a world without calculators. And not much has changed. Quick idea for teaching regr...

While teaching measures of central tendency in Intro stats, I have shrugged and said: "Yeah, mean and average are the same thing, I don't know why there are two words. Statisticians say mean so we'll say mean in this class." I now have a better explanation than that non-explanation, as verbalized by this podcast: The average is thrown around colloquially and can refer to mode, while mean can always be defined with a formula. This is a fun podcast that describes mode vs. mean, but it also describes the research the rabbit hole we sometimes go down when a seemingly straightforward question becomes downright intractable. Here, the question is: What is the modal American? The Planet Money Team, with the help of FiveThirtyEight's Ben Castlemen, eventually had to go non-parametric and divide people into broad categories and figure out which category had the biggest N. Here is the description of how they divided up : And, like, they had SO MANY CELLS in their des...

I usually blog about funny examples for the teaching of statistics, but this example is for teachers teaching statistics. Normile, Bloesch, Davoli, & Scheer's recent publication, "Introducing the new statistics in the classroom" (2019) is very aptly and appropriately titled. It is a rundown on p-values and effect sizes and confidence intervals. Such reviews exist elsewhere, but this one is just so short and precise. Here are a few of the highlights: 1) The article concisely explains what isn't great or what is frequently misunderstood about NHST. 2) Actual guidelines for how to explain it in Psychological Statistics/Introduction to Statistics, including ideas for doing so without completely redesigning your class. 3) It also highlights one of the big reasons that I am so pro-JASP: Easy to locate and use effect sizes.