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...

Katherine Baicker ,  Oeindrila Dube ,  Sendhil Mullainathan ,  Devin Pope,  and  Gus Wezerek created an interactive, data-driven piece for NYT . It provides a new perspective on how we should proceed with re-opening businesses during the COVID-19 pandemic. They argue that we must consider 1) how long people linger in different types of stores, 2) how often they visit these stores, 3) the square footage of the stores, and 4) the amount of human interaction/surface contact associated with how we shop at different stores.  How to use this in class:    1) Show your students how data can inform real-life problems. Or crises, like how to safely re-open stores during COVID-19. 2) Show your students how data can be used in creative ways to solve problems. The present argument uses cellphone location data. 3) Show your students data viz in real life: Here, scatterplots that really improve the #scicomm potential of this piece. 4) Show your students the rese...

This brief video from NPR (they make videos, what?) describes how the CDC will be randomly sampling Atlanta residents to test for COVID-19 antibodies. The efforts hope to provide a better estimate of the spread of the disease. H/t to Sy Islam for sharing this with me. I think you could use this in class as a super-fast example of how we use samples to generalize about larger populations. The CDC is sending out employees to conduct antibody tests on a random sample of Atlanta residents. The tests are meant to show how many people have been infected with the coronavirus. pic.twitter.com/mXqznHUJmV — NPR (@NPR) April 29, 2020

Hi! Here is an easy-to-use review of regression, with a regression example. I posted about using this catty tweet to teach regression previously: Right? Do you see the regression? Every wife has two data points: Year she gets married, and the year she was born...and from that, you can perform a regression to predict when Quaid would actually marry someone who has not been born yet (2052). Well, I decided to make it into a whole Google Slides presentation for this example, with links to data, to use as a regression review during the quarantine. Admittedly, the example is ridiculous, and the sample is far too small to run a reliable regression. That being said, I think the example is vivid and sticks. I also think it does an excellent job of illustrating how the equation can be used to make predictions. Additionally, I genuinely find meaning in helping out my fellow statistics instructors in good times, and doubly so during this challenging semester. Feel free to view my pres...

Here are two good review examples for chi-square, one for goodness-of-fit, and one for the test of independence. Here is my Google Slide presentation, which includes links to data sets for the examples. One features Taco Bell . The other features actual Developmental Psychology research, as featured on NPR . When I use these in class, the students have already been introduced to chi-square, have been walked through examples of both chi-squares, and then they analyze the data on their own using JASP. Y

I hope everyone is hanging in there. Here is a pretty straight forward one-way ANOVA example that is interactive, based on for-real personality psychology research, and interesting. I blogged about this previously but whipped up a Google Slideshow you can download and edit to suit your own teaching. Also, I uploaded data that you can use with your students.  TL:DR- A bunch of researchers gave the NEO to 1.5 million Americans to determine if different regions of the US have different personality trends (see research here ).  Original Study They do. Then Time magazine reported on the study . And the scicomm was beautiful. They accurately described the research AND created a fun interactive portion in which you students can take the NEO-Short Form and be matched with the state that best matches their personality (Hi, I am West Virginia because I'm high in neuroticism and low in openness to new experiences, which are great qualities to have during a pandem...

I apologize in advance if you are pandemiced out. It is just that my brain won't stop seeing stats examples in information related to the COVID-19 pandemic. For instance, researchers are looking back at the 1918 Flu Pandemic in order to forecast how social distancing (or lack thereof) will affect mortality rates now. And these patterns, as illustrated by National Geographic, demonstrate different data distribution shapes . The data comes from a reputable source, is scaled to deaths per 100,000 as to allow for comparison, and the distributions are related to very important data. Other lessons your students can learn from this data: This is what good scicomm looks like. Also, sometimes a good data visualization is better than an accurate-yet-filled-with-jargon version of the same information. For instance, much has been shared about NYC vs St. Louis in terms of timing of quarantine. Here is the comparison yet again, but in an easier-to-follow description: There is a ton of...