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...

 Unlike the Chartr newsletter, I love this as it feeds my fascination with data and provides interesting examples for the class. As I sit here writing (5/11/24), I am enjoying my other favorite stats newsletter, How to Read This Chart . The current newsletter discusses data visualizations used on the front page of the Post. Such as: Philip Bump lovingly curates this newsletter. One time, he found historic, unlabeled charts and asked readers for help interpreting them . I also thought this one, which compared the margin of error and sample sizes used by major national polling firms, fascinating .

My friends. I started a podcast.  I've created the Not Awful Data podcast with the help of Garth Neufeld and Eric Landrum at the Psych Sessions podcast empire.  Why? I try to keep my blog posts brief and to the point, but I also love to discuss exactly how I use my favorite data sets in the classroom. This podcast will let me discuss and highlight some of the data sets I've shared on my blog and provide more information on exactly how you could use them in class. Anyway. Every podcast if five minutes long. I plan on posting a new episode once a week. My hope is that this will re-introduce you to some of my older resources and provide you with some out-of-the-box resources you can use in your own teaching. Here is a link to my first episode , which recaps the horror movie/heartbeat data I shared on the blog recently. The podcast is also available on Spotify .

Chartr|Data Storytelling   Just subscribe. It is entertaining. I mean, look at this: Like, there is a part of my brain that can just doom scroll stats content. Stats scroll? That sounds like an R function. Anyway, that part of my brain loves Chartr

Every Spring and Summer, I become obsessed with the Merlin App. This app allows you to record bird songs using your phone and then uses machine learning to identify the bird call. The app can also do visual IDs if your phone has a much better camera than mine.  It is like PokemonGo. I have to catch them all. But no augmented reality, just reality reality.  Here is my "life list" of all the birds I've identified in about a year of using the App: This app brings joy. It is also a quick example of how citizens can become scientists, how Apps can generate data from citizen scientists, and how machine learning makes it work. So, this isn't a lengthy example for class, but it is an accessible example that shows how apps and phones can be harnessed for the better good. And science is super fun. How this App gathers data from users: But how? Via machine learning: Here is even more info on how their machine learning works: AND THEN, the data can be used for scientific research...