This example illustrates how better assessment has likely increased in autism diagnoses (as opposed to the increase being due to vaccines or hysterical parents). It does a good job of illustrating truncated y-axes and the surveillance effect. It also reminds our psychology majors that we have many professional allies and colleagues outside of psychology. Like speech language pathologists.  I found these examples (see below) on Facebook from speech-language pathologist/excellent science communicator  Mrs. Speechie P.   AKA Andi Putt. How to use in class: 1. Truncated y-axis I like how she mentions that truncated y axes can be a scare tactic. I also like that she shows there are still relatively few in the total population. https://www.facebook.com/photo/?fbid=1327073175898079&set=a.463959318876140 On this theme, she shared a second image that does a really good job of showing how proper diagnosis isn't the same thing as fake/inflated diagnoses (a common argument in ant...

A research study counted how often ChatGPT made up citations for three different categories of mental disorders (binge eating, body dysmorphic, and major depressive). They used a chi-square to determine if rates of made up citations differed by disorder (they do).  If ever there was an article that belonged on this blog, this is it. You can use it in your stats class as an example of chi-square and/or as a warning to students if you ask them to perform literature reviews for your class. The original paper, Influence of topic familiarity and prompt specificity on citation fabrication in mental health research using large language models: Experimental Study was published in December 2025, and summarized by PsyPost  shortly after publishing.  What the researchers did: What the researchers found: How to use in class: 1. This is a good chi-square results section. They shared the test value and the p value, of course, but I like how they shared the varying rates of inaccuracy...

This spicy example explains scales of measurement, continuous vs. categorical variables, and how you can measure and quantify anything.  Uncommon Goods sells quirky gifts. While I was looking for Christmas gifts last year, I came across this kit https://www.uncommongoods.com/product/scoville-scale-chili-pepper-tasting-kit#618780000000 I have a teenage son, and teen boys love this sort of stuff. Actually, I think spicy peppers are enjoying increased popularity due to the Hot Ones show (The show where celebrities eat increasingly hot chicken wings while being interviewed, like Jennifer Lawrence and her famous GIF from the show).  Maybe you could link this example back to that show? Welcome to how my brain works. Anyway, among the information Uncommon Goods shared about this kit was an image of the packaging for the kit, detailing the Scoville scale rating for each pepper: And my stats teacher brain translated this packaging into this, since the same data (hottness) is presented ...

TL:DR: Sugary drinks might up your risk for oral cavity cancer (so says relative risk) but probably won't be the thing that kills you (so says absolute risk).  In depth: I love teaching applied statistics, including showing my students how to identify and properly attention-grabbing examples of relative risk ( 1 , 2 ). HOWEVER, relative and absolute risk aren't lying. But they can scare people, so I think it is important to share both calmly.  This example from JAMA Otolaryngology is a good example of how to responsibly share relative and absolute risk. It has a very calm, non-click bait article title:  https://jamanetwork.com/journals/jamaotolaryngology/fullarticle/2831121 Cool. Also, thanks for using female research participants. Next, the results are described in a non-salacious manner, with the absolute risk in red and relative risk in blue. How could you use this in class? Sharing relative risk isn't in and of itself unethical. Using it to scare people is questi...

I came across this article on social media: https://www.pimlicojournal.co.uk/p/mps-are-almost-certainly-using-chatgpt This got my attention, because I'm sick of people ragging on college students using AI. EVERYONE is using AI. That doesn't mean it is always OK or evil, but let's stop ragging on the kids. Anyway, the author used data to make their claims via z scores: https://www.pimlicojournal.co.uk/p/mps-are-almost-certainly-using-chatgpt Ways to use in class: 1. I like to talk to students about data as evidence. In science, it can be evidence to reject or not reject a hypothesis. In real life, it can track trends, both innocuous and suspicious. 2. This is another way of talking about z scores, a crucial but less exciting aspect of basics statistics. As best as I can tell, this was the z score formula used:  frequency z score = (number of times phrase was used in a year - mean times the phrase was used in all years)/standard deviation of number of times the phrase was...

No, that is not a dig at her romantic relationships. It is instead a question about the impact of her numerous variants on descriptive data in the music industry. And if you found your way to this blog, you know that I love a good, relevant, pop-culture-driven example for explaining statistical concepts. Especially somewhat dry concepts, like the assumption of independence when collecting data. Anyway. Per Microsoft Copilot: Across all formats, there are 34 different versions of the album: 8 vinyl 18 CD 1 cassette 7 digital Microsoft Copilot. (2025, November 8). Response to query about Taylor Swift’s album variants [AI-generated response]. Microsoft Copilot. https://www.reddit.com/r/TrueSwifties/comments/1n04kdu/the_life_of_a_showgirl_vinyl_variants_announced/ When overall album sales are counted by major industry players (Billboard, Luminate), every variant sale counts as one sale.  So, she is  selling many albums, but it raises the question of whether her sal...

Goodhart's Law is a truism in assessment circles, which are always statistics-adjacent. And that is why I'm sharing this fine embodiment of Goodhart's Law on my blog. Always pair the important stuff with something ridiculous, I swear, it makes it easier to remember the important stuff.

 There are so many ways to use this tool: Nathan Yau's Flowing Data is one of those websites I check every few days for statistical inspiration. He shares  the work of others and his own, including this  interactive bee swarm plot that illustrates salaries  for various  jobs. The bee plot, with the cursor of Psychology Teachers. https://flowingdata.com/2025/09/09/salary-and-occupation-2024/ There are many ways to use this in stats class: 1. At some point, you should talk about career exploration with your students.   2. Statistics students should be learning about modern data visualizations like this jitter plot, aka bee swarm plot.  3. If you cursor over any dot, you can see the 25th and 75th percentile scores and n size for that occupation's salary. 4. The size of each circle corresponds to the n size. Which I love because jitter plots do a great job of illustrating variability in a data set. However, each data point here represents an average...

This blog post contains one small analogy for explaining family-wise error to your students. I was making French toast for dinner the other night.  While I was measuring out cinnamon, I realized using one tablespoon instead of three teaspoons to avoid measuring errors is sort of like using a one-way ANOVA with three levels instead of doing three  t  tests to avoid Type I error.   Stick with me here. If I were to use three teaspoons to measure out an ingredient, there is a chance I could make a mistake three times. Three opportunities for air pockets. Three opportunities to not perfectly level out my ingredient. Meanwhile, if I just use one tablespoon, I will only risk the error associated with using a measuring spoon once.  Similarly, every time we use NHST, we accept 5% Type I error (well, if you are a psychologist and using the 5% gold standard, but I digress). Using three tests ( t tests) when we could use one (ANOVA) will increase the risk of a false positi...

Beth Morling is my friend and fellow author at W.W. Norton & Co. Recently, we thought of each other when a news story came out about the repatriation of the remains of 19 Black New Orleanians whose skulls were used for racist phrenology research in Germany in the 1880s. It made us think about the various forms that self-correction can take in science, as well as the importance of adhering to the scientific method.  Check it out here .

This YouGov data asked Americans to rate how much they enjoy a variety of summer activities. They graphed out the percentage of people, divided by demographics, who indicated that they like or love a summer activity. One of the demographics they used was age. Which makes me feel seen, and I can already imagine how I will poke fun at myself, a 46-year-old who hates outdoor sports. More  pedagogically, I can use this data when introducing paired  t -tests. Specifically, I can get them to ponder this data and think about why  the age differences exist.   Here is the data visualization for activities where there is a big age gap in enjoyment: Here is the data visualization for activities where there is not a big age difference: I think they really missed out by not including birdwatching on this list. I'm 46 and I hecking love it.  I could also see this as an example in a Developmental or Psychology of Aging course. What is driving the differences between older...

This summer, the Psi Chi Journal of Psychology Research published  Rouse, Russel, and Campbell's Beyond the textbook: Psi Chi Journal articles in introductory psychology courses. It is a curated list of paywall-free Psi Chi articles, mostly with student co-authors, that are peer-reviewed and of an appropriate writing level and length to use in an Introduction to Psychology course. The authors provide the following information for each of the articles: In addition to being appropriate for Into Psych, these articles are also perfect for Intro Stats. In my classes, I emphasize the ability to read and write simple result sections. One way I would review this skill is by showing my students Results sections from published research and asking them to identify the test statistics, effect size, and other relevant information. This selection of articles features clear and concise results sections for t -tests, ANOVA, factorial ANOVA, regression, and correlation. I created a spreadsheet...

 I'm surprised I haven't shared this in this space already. It is one of my favorite data points ever. Clearly, I have favorite data points. https://www.economist.com/graphic-detail/2019/07/04/are-extraterrestrials-extra-patriotic How to use in class? 1) There is data for EVERYTHING if you look hard enough, 2) WHY might this relationship exist (heat stroke, staring at the sky, drinking, freedom, fireworks)?  If you like this example, check out my W.W. Norton & Co. textbook,  Psychological Statistics for Everyone . 

This one is for all of my psychometric instructors. CNN created an engaging, interactive website to explain rank choice voting using ice cream flavor preference.  It was created due to the 2025 NYC mayoral primaries, but uses ice cream instead of humans to make for a good explainer that may have a home in your classroom. https://www.cnn.com/interactive/2025/06/politics/ranked-choice-voting-explained-dg/ First, you rank order your top five favorite ice cream flavors out of a field of ten. Then, you can view all users' ranking data, and see how the distribution changes when the least popular flavor, Rocky Road, is eliminated and the rocky road voters' votes are redistributed. The vote relocation goes on and on... Finally, you get to see the winner, chocolate. Rank-choice voting is one of those concepts that is easier to explain with a bit of animation and a very simple premise. I couldn't capture it in my screenshots, but the flavor elimination and redistribution are animated...

While most of my class time is dedicated to the specifics of performing and interpreting inferential tests, basic statistical literacy and thinking are equally important lessons. Here are some of the big-picture literacy ideas I want my students to think about in my stats classes: 1. How can we use data to understand patterns to make predictions? 2. How can we separate the signal from the noise?  3. How can data actually inform real life and current events? 4. How can we repurpose existing data in a world where data is everywhere? Here is an example I JUST found that addresses all of these ideas. The  Pentagon Pizza Report is an X account that monitors Google "Popular times" data in pizzerias near the Pentagon to predict military activity.  The X account asserts that unusually high, later-than-normal foot traffic at pizzerias near the Pentagon (x) may indicate that Pentagon military staff are working late and need to grab take-out for dinner(y).  Most recently, the...

I've been writing this blog, statistics pedagogy articles, chapters, and a whole statistics textbook for over ten years. I'm at the point where I see silly stuff on the internet, and it automatically translates to a statistics example. Like this recent Tweet from Sheetz about the Pirates/Philly series this weekend. https://x.com/sheetz/status/1923397811778785489 This is an unapologetically Western PA tweet. I will be using it as a chi-square goodness-of-fit example with my Western PA students at Gannon University this Fall. I even created a data file that mimics the findings (Methods:  n  = 380, Results: p < .001. Conclusion: Sheetz followers on Twitter love some curly fry). If you are a poor, unfortunate soul who has never enjoyed treatz from Sheetz, I feel bad for you. Look up your favorite regional brands on Twitter and translate one of their polls into a chi-square example. Or travel to your nearest Sheetz to experience some damn joy. 

State Farm, a prominent American insurance provider, shared data that ranked American states based on the number of animal-related (mostly deer) car accident claims filed per state .  I blogged about this data previously , and I am returning to it now as part of my semi-regular Discussion Board Ideas series on this blog. I have been using this prompt in my online stats class in NW PA for about a year now. I'm going to share some of that success here. Note: PA is #4 for deer-related car accident claims, so this data resonates with my students.  I use this for the fifth of seven weeks in my online class, so the students are comfortable with the class format and one another by then. Here is the exact prompt I use: I have a weird question for you: How do you think Pennsylvania ranks when it comes to the number of car accident insurance claims involving colliding with animals? Yes, I am on my soapbox about safe night-time driving in PA. Once you have your guess, check against...

A clever YouTuber, PWA , built a channel with nearly a million followers based on animated videos that compare nations based on data. Every nation is a sassy sphere. Each grows and shrinks in size, in comparison with other nations, as the data is presented. Like this image, illustrating national debt as a portion of GDP... I swear, it is funny and engaging without trying too hard. Also, for better or worse, framing data sharing and visualization as a thing that can make you a successful influencer WILL grab your students' attention. I think these videos would make good  bell-ringer s ( TM Janet Peters) for the start of your class. This influencer makes a ton of videos, and they aren't all related to data, FYI. Here are a few good examples for Stats class:  National Debt: In this clip, the countries compare their national debt. This video discusses some of the choices statisticians make with their data. For example, they compare their national debt in USD and then compare...

Look at how Dima Yarovinsky turned the Terms and Conditions documents for several social media platforms into foreboding and beautiful art/bar graphs illustrating how much we sign away without reading. Note: He even uses the X axis to describe the length of and reading time for each T&C statement!  I think data is beautiful. This example does a good job of showing the beauty and impact of good data visualizations to my students. This isn't a huge example to use in class, but I will use it next time I discuss bar graphs. For more from the artist, in his own words, visit his webpage .  For a thought review of this art, see this article by Emma Taggart .

Does anyone else teach correlation and regression together at the end of the semester? Here is a treat for you: Updated data on Leonardo DiCaprio, his age, and his romantic partner's age when they started dating. A few years ago, there was a dust-up when a clever Redditor r/TrustLittleBrother realized that DiCaprio had never dated anyone over 25. I blogged about this when it happened. But the old data was from 2022. Inspired by this sleuthing,  I created a wee data set, including up-to-date information on his current relationship with Vittoria Ceretti, so your students can suss out the patterns that exist in this data.