I love data-informed opinions and arguments. So, I was fascinated when NPR told me that some academics quietly take side gigs in which they use data to help tobacco companies. Specifically, tobacco companies argue that, over time, people have become more and more aware of the risks associated with smoking. As such, Big Tobacco argues that they should not be held responsible for the harm caused by smoking. From NPR: I went down the rabbit hole to find the original data and more information on Gallups position, and this is what I found: https://news.gallup.com/poll/1717/tobacco-smoking.aspx So, while American's had heard about the potential connection between cancer and smoking, not everyone believed that this was true (41%), and many people weren't sure about the link (29%). How to use in class: -Data used in court. -Data is used by historians. More here:  http://www.stat.columbia.edu/~gelman/stuff_for_blog/Ethics-of-Consulting-for-the-Tobacco-Industry.p...

I teach many, many statistics classes. In addition to the core topics typically covered in Introductory Statistics, I think covering real-life controversies involving statistics is vital. Usually, these are stories of large organizations that attempted to bias/PR attack/skew/p-hack/cherry-pick data to serve their own purposes.  I believe that these examples serve to show why data literacy is so critical because data is used in so many fields, AND our students must prepare themselves to evaluate data-based claims throughout their lives. I put out a call on Twitter , and my friends there helped me generate a great list of such controversies. I put this list into a spreadsheet with links to primers on each topic. This isn't an in-depth study of any of these topics, but the links should get you going in the right direction if you would like to use them in class. I hope this helps my fellow stats teachers integrate more applied examples into their classes. If you h...

Create an account with Pew Research, and you can download some of their data sets, including a) syntax files, b) detailed methodology, and c) codebook, including detailed screenshots of what the survey felt like to participants.  I think there are three ways to use this in class: -Show your students what proper data documentation looks like -Get some data, run some analyses -Get some data, look up Pew's reports based on the data, see if you can replicate the findings. How to Properly Document Your Research Process. Pew documents the hell out of these data sets. Included are: Syntax files: Methodology: Surveys, featuring the questions but also screenshots of the user experience: Get some data, run some analyses. MY FIRST EVER FACTOR ANALYSIS EXAMPLE, y'all. Per the methodology documentation, Pew creates its own scales. Within this data set (American Trends Panel Wave 34), they use several scales to measuring attitudes about medical treatments. ...

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...