I started this blog with the hope of making life easier for my fellow stats instructors. I share examples and ideas that I use in my own classes in hopes that some other stats instructor out there might be able to incorporate these ideas into their classes. As we crash-landed into the online transition last Spring, I created took some of the blog posts and made them into lengthier class lessons, including Google Slides and, when applicable, data sets shared via my Google Drive. I ended up with four good lessons about the four big inferential tests typically cover in Psych Stats/Intro Stats: T-test, ANOVA, chi-square, and regression. I think these examples serve as great reviews/homework assignments/an extra example for your students as they try to wrap their brain around statistical thinking. As we are preparing for the Fall, and whatever the Fall brings, I wanted to re-share all of those examples in one spot. Love, Jess ANOVA https://notawfulandboring.blogspot.com/2020/04/online-day-6...

People I love very much live in Florida. My very favorite academic conference is held in Florida. I want Florida to flatten the curve. But Florida is flattening the curve. Believe me when I say that I'm not trying to dunk on Florida, but Florida has provided me with prime material for statistics teaching. Timely material that illustrates weaponized data. Some examples are more straightforward, like median and poor data visualization. Others illustrate a theme that I cover in my own stats class, a theme that we should all be discussing in our stats class: Data must be very, very powerful if so many large organizations work so hard to discredit it, manipulate it, and fire people who won't. You should also point out to your students that organizations working so hard to discredit are typically straightforward descriptive data, not graduate-level data analysis.  1. Measures of Central Tendency As of June 23, the median age of people newly diagnosed with COVID-19 in Florida dropped ...

In our stats classes, we need MANY examples to convey both theories behind and the computation of statistics. These examples should be memorable. Sometimes, they can make our students laugh, and sometimes they can be couched in research. They should always make our students think. In this spirit, I've collected three small examples from the Pew Research Center's  Race and Ethnicity  archive (I hope to update with more examples as time permits). I don't know if any data collection firm is above reproach, but Pew Research is pretty close. They are non-partisan, they share their research methodology, and they ask hard questions about ethnicity and race. If you use these examples in class, I think that it is crucial to present them within context: They illustrate statistical concepts, and they also demonstrate outcomes of racism.   1. "Most Blacks say someone has acted suspicious of them or as if they weren't smart" Lessons: Racism, ANOVA theory: between-group dif...

Kyne is a drag queen, a contender on Canada's Drag Race (which will air in July of this year). She also posts lots of photos displaying her exceptional make-up and costuming skills. Source:  https://www.instagram.com/p/CAlWzU1AN0d/ She also LOVES statistics and math. Better yet, she has a talent for concise and straight-forward explanations of math and statistical topics. Perfect for teaching stats. Kyne posts most of her math content directly to TikTok , but also maintains a channel for her math content on Instagram . Below, I've compiled some of her stats content:  Misleading graphs, and also very important, thinking your way through misleading graphs (CNBC graph that exaggerated job growth in the US).  Several posts ( 1 , 2 , 3 ) break down and describing the flawed reasoning present in several viral posts about crime rates, data, and race. I think these posts are especially helpful for novice statisticians and she walks the viewer through the logic of the data.  ...

Today's post is one, small, to-the-point example of sampling bias. On May 27, 2020, my family and I were awaiting lift-off for the (subsequently grounded) NASA/SpaceX launch. To no one's surprise, I was following NASA on Twitter during the hoopla, and I noticed this Tweet: https://twitter.com/NASA/status/1265724481009594369 And I couldn't help but think: That is some sampling bias. Admittedly, their sample size is very impressive, with over 54K votes. But this poll went out to a bunch of people who love NASA so much that they follow it on Twitter.  What is a less biased response to this question? As always, Pew Research Center had my back. 58% of Americans responded that they definitely/probably weren't interested in traveling into space: https://www.pewresearch.org/fact-tank/2018/06/07/space-tourism-majority-of-americans-say-they-wouldnt-be-interested/ If you want to expand upon this example in class, you could ask your students to Google around for information on the ...

The Washington Post , and numerous other media outlets, recent wrote about an unintended consequence of COVID-19 and the sudden drop off in commercial flights: Fewer data points for weather forecasts ( PDF ). Due to the coronavirus, commercial flights are down: How does this affect weather forecasts? Data is constantly being collected from commercial flights, and that data is used to predict future weather: Ways to use in class: A conceptual example of multivariate modeling : Windspeed...temperature...humidity...lots of different data points, from lots of different elevations, come into play when making our best guess at the weather. This is a non-math, abstract way to discuss such multivariate models. A conceptual example of effect sizes/real-world effects: In the article, they clearly spell out the magnitude of the data loss. That is pretty easy to track since we can count the number of flights that have been canceled. More complex is determining the effect size of this data loss....

Hi! This post is a change of pace. Instead of providing an example to use in stats class, I'm going to share how I incorporated text-message based class discussion in online courses with the GroupMe App. Doing so was a big win for me during a hard semester, I hope it is a big win for anyone who happens to read this post and use GroupMe in the future. My experience using GroupMe App to facilitate class discussion during The Rona My goals for OL SP20: I wanted my students to learn. I wanted to preserve the best parts of my classes. I didn't want my classes to be another burden in a stressed out world. During March 2020, I wast teaching Introduction to I/O Psychology. It was a class of 20. My students were mostly Juniors and Seniors, who were either psychology majors or minors. On our last day of f2f class, when we knew that we were going to transition to OL, I asked my students to reserve our normal 12:20-1:25 MWF meeting time for the class. I wanted my students to continue to h...