As a stats nerd, I love it when clever people make lives easier by finding clever, easy, indirect ways to estimate the thing they want to measure. As a statistics instructor, I find such examples engaging, as they encourage students to think critically and nurture their statistical literacy.  Like the Waffle Shop index. TL;DR: During weather emergencies, the federal government tracks whether or not Waffle Shops are open as a proxy for the severity of damage in a community. Waffle Shops are tough as hell, and if they close, a community needs help.  Below is a map of Waffle Houses. https://www.scrapehero.com/store/wp-content/uploads/maps/Waffle_House_USA.png Due to Hurricane Beryl, the people of Houston, Texas discovered an even more accurate measure the severity of electricity outages: The Whattaburger Index:   https://www.facebook.com/photo/?fbid=8242206945824619&set=gm.2698315720337038&idorvanity=1416658058502817 Certainly, Waffle House exists in Texas. 126...

Weather prediction is data. This makes weather data-related stories and examples highly relatable. The Washington Post published an interactive article t hat shows how accurate weather predictions are for a given city in the United States. This means that we, stats instructors, can use this page to provide a geographically personalized lesson on weather prediction, the limitations of data, and why predictions about the future are only as good as the consistency of the past. I also like this example because it isn't terribly mathy and encourages statistical literacy.  Kommenda and Stevens, writing for the Washington Post, recently shared a story on the accuracy of weather predictions based on time away from the target day. Here, the DV is prediction accuracy, operationalized using the difference between predicted and actual high temperature. You could always ask your students how they would operationalize weather...or maybe some weather matters more than others? Folks in Erie...

Okay. I haven't used this in class yet because it is July, and I just found it. However, I will open the Fall 2024 semester with this example. It is fun and accessible and shows how research can be used to study whether or not personality varies based on astrological signs. I will start by showing them a bunch of funny astrology memes (see above). Then, I'll ask them to think of ways to design a study to prove that astrology is/is not bunk. What sort of data would they need to collect to do this?  Then, I'm going to show them this study ( Joshanloo, 2024 ): https://onlinelibrary.wiley.com/doi/epdf/10.1111/kykl.12395?domain=author&token=BKSRDREAX9F3BKAWGVBD Statsy things to share with your students: 1. Archival data : The used repurposed, vintage, federal data. The General Social Survey, to be specific. Data scientists are trained to see the potential of random data sets.    The horoscope sign was simple to determine since the GSS collects birthday data. The author was...

I mean. This is the most entertaining research methodology I have ever seen. What did this look like? This is what it looked like.  So, this is barely a statsy example, but it does include data outcomes:  n = 175, with some snakes striking the boot ( n = 6) and some coiling ( n = 3). While PIs might try to No IRB would let you get away with asking your graduate student to step on snakes. Mostly, this is funny. I found his research, too . While I think the fake leg is highly amusing, I think it is great that Morris is a passionate advocate for snake education and teaching people to be tolerant of snakes they find in the wild. Finally, I heard about this research on an NPR story about snake handling classes (taught by Morris) in Arizona. A WHOLE CLASS. 

We need more nonsignificant but readily understood examples in our classes. This correlation/regression example from Information is Beautiful  demonstrates that the calories in delicious caffeinated drinks do not correlate with the calories in the drink. Caffeine has zero calories. The things that make our drinks creamy and sweet may have calories. Easy peasy, readily understood, and this example gives your students a chance to think about and interpret non-significant, itty-bitty effect size findings.  Click here for the data. Aside: Watch your language when using this example. We need calories to stay alive and none of these drinks, in and of themselves, are good or bad. Our students are exposed to way too much of that sort of language and thinking about food and their bodies. What they choose to drink or eat is none of our business. When I share this visual, I omit the information on the far right (exercise) and far left (calorically equivalent foods). It distracts from the...

A quick, accessible example of the Law of Large Numbers. Using candy. Reddit user Jeffrowl counted the proportions of M&Ms across multiple bags, and you can see the proportions of colors reflect the true underlying population as the number of bags increases.  Here is the link , and a screenshot of the GIF can be seen here: I don't use the M&M probability example in class, but  many of you do . This is a nice addition to that example, but it also serves as a brief, standalone example. ALSO, to my nerdy delight, the author's responses include a Methods section: ...as well as information on baseline data: 

I am an Air Force Brat. Growing up, my dad used to talk about all of the services the USAF provides to our country and the world. It employs many  musicians , advances  airplane safety  for civilians, and conducts and sponsors plenty of research . This post will focus on the USAF's unique position to advance weather and climate science via data collection in big, honkin' airplanes that can fly through hurricanes.  Weather forecasting requires data. As reported by Debbie Elliot for NPR , the Air Force collects data that, specifically, will help us better predict severe weather and save lives.  Aside: This whole mission started on a bet: HOW TO USE IN CLASS: -I tell my students repeatedly that I'm not trying to turn them into the world's best statisticians. I'm trying to help them learn how to be themselves, with their interests and abilities, but fluent in statistical literacy. This lesson goes better when I can have examples of data jobs that aren't traditi...