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...

We should teach with data sets representing ALL of our students. Why? You never know what example will stick in a student's head. One way to get information to stick in is by employing the self-reference effect .  For example, students who grew up in the country might relate to examples that evoke rural life. Like getting the first day of buck season off from school and learning how to watch out for deer on the tree line when you are going 55 MPH on a rural highway. Enter State Farm's data on the likelihood, per state, of a car accident claim due to collision with an animal (not specifically deer, but implicitly deer) . Indeed, my home state of Pennsylvania is the #3 most likely place to hit a deer with your car. State Farm shares its data per state: https://www.statefarm.com/simple-insights/auto-and-vehicles/how-likely-are-you-to-have-an-animal-collision I am also happy to share my version of the data , in which I turned all probability fractions (1 out of 522) into probabili...

  The Science of Scare project, conducted by MoneySuperMarket.com, recorded heart rates in participants watching fifty horror movies to determine the scariest of scary movies. Below is a screenshot of the original variables and data for 12 of the 50 movies provided by MoneySuperMarket.com: https://www.moneysupermarket.com/broadband/features/science-of-scare/ https://www.moneysupermarket.com/broadband/features/science-of-scare/ Here is my version of the data in Excel format . It includes the original data plus four additional columns (so you can run more analyses on the data): -Year of Release -Rotten Tomato rating -Does this movie have a sequel (yes or no)? -Is this movie a sequel (yes or no)? Here are some ways you could use this in class: 1. Correlation : Rotten Tomato rating does not correlate with the overall scare score ( r = 0.13, p = 0.36).   2. Within-subject research design : Baseline, average, and maximum heart rates are reported for each film.   3. ...

I have shared  AMPLE examples for teaching correlations . Because I've got you, boo. Like, I have shared days' worth of lecture material with you, my people. I am adding one more example. I have used this example in my positive psychology course for years, and it really illustrates what can happen en masse when marketing departments and less-savory pop-psych elements try to establish causal relationships with features (stereotypes?) of happy countries and individuals' subjective well-being. I like this one because it is math-free, UG-accessible, and not terribly long. Joe Pinsker, writing for the Atlantic, argues that... https://www.theatlantic.com/family/archive/2021/06/worlds-happiest-countries-denmark-finland-norway/619299/ TL;DR: Just because Northern European nations consistently score the highest on global happiness data doesn't mean that haphazardly adopting practices from those countries will make you happy. Correlation doesn't equal causation. H ere is the ...