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Showing posts from 2026

Using GenAI to generate teachable data sets (here, an independent t test)

Two things I love to use when teaching stats are: 1)  Journal of the American Medical Association (JAMA) visual abstracts . I've blogged about them before. 2) Useful tools to generate pretend data sets that mimic real data, and use those pretend data sets to teach. See: Richard  Landers '  and Andrew Luttrell 's websites. So, I was delighted when I saw this recently posted visual abstract about  Ewing-Cobbs et al. (2026) research on using a specific CBT program to reduce stress in children following a traumatic physical injury .  https://jamanetwork.com/journals/jamapediatrics/fullarticle/2848163 I have a new  example of an independent  t  test  for class. Yay! And I teach tons of future nurses/PAs, so it is doubly applicable. However, the authors stated that the data wasn't immediately available. Also, once it is available, they (very reasonably) want to track their data sharing. Meaning that even if I could get their data, I shouldn't be s...

Young adult suicide is down, as demonstrated by regression.

This is an article about a very sensitive topic, but it is also a hopeful article (young adult suicide seems to be on the decline in the US, and there is reason to believe that it is due to the introduction of the 988 hotline .) Here is a  link to the original research study published in JAMA Network , and here is a link to  Scientific American's write-up on the research . In my class, I emphasize that regression has a lot in common with correlation, but adds prediction. I emphasize it so much that I used it in the name of my regression chapter in my textbook . As such, I was delighted to find this  excellent, psychology-related example of how past data was used to predict the future. But the future is the present? And the predicted data lives in an alternative timeline where the 988 mental health crisis hotline never existed in America. Anyway, TL;DR: Young adult suicide is on the decline (hooray!!) in America, and this research a) uses fancy regression to demonstrate th...

Statsplanations from Sketchplanations

Two of my very favorite statistics comic sources are  Saturday Morning Breakfast Cereal and XKCD.    I have been following them for years, but more recently, I came across Jono Hey's Sketchplanations . These aren't ha-ha comics as much as they are concise explanations of complex concepts. And some of those explanations involve statistics and would look lovely in your class lectures. This is perfect for those of us who teach JASP and try to explain the dangers of misspelling words used as nominal variables.  Also, he has a few that provide images to go with some of our favorite data quotes: Here are full entries under the comic's  statistics  and  data  tags.

One small, psychological ANOVA example you can use in class.

This is just a little one-way ANOVA with three levels. You can use it in class to assess, review, or teach the topic. It comes from the following article by Rivera-Chavez et al . https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2843427 TL:DR- They gathered data and performed a one-way ANOVA that suggests that people with emerging psychosis have glutamate (a neurotransmitter) levels that are higher than both controls and folks who have schizophrenia diagnoses. Even if you aren't an expert on this topic, JAMA's ready to explain the relevance of this study to your students: Reasons why I love this as an example for my novice psychological statisticians: 1. This data is related to psychology, a simple one-way ANOVA with three levels, and was recently published, making it a nice little refresh to my course content. There are other analyses in the article, but here are the ANOVA results. 2. I emphasize that my students learn how to read and write statistical findings, so h...

A McSweeney's Short Imagined Monologue, written from the viewpoint of correlation murdering causation.

You guys. Remember McSweeney's ? Your undergraduates won't get why this  Short Imagined Monologue, written by correlation, is absurd and funny, but maybe your graduate students will.   https://www.mcsweeneys.net/articles/im-correlation-heres-why-i-shot-causation-at-a-harvard-medical-school-conference

Mr. Beast gave us a real-life bee swarm plot.

Hey, I have kids, so I knew that Mr. Beast made a video where 100 competitors, one person from every age from 1-100, competed in feats for $250K.  In the very first competition, competitors ran a footrace, and the top five in each age category advanced to the next round.  Image from: https://www.reddit.com/r/data_irl/comments/1r15ecq/data_irl/ Anyway, in doing so, Mr. Beast inadvertently created a jitter plot using humans. Age group/starting line is at the top of the image, with the checkered finish line at the bottom. The dark blue/light blue columns are a nice touch, too. How to use in class: 1) Pander to your students by using a Mr. Beast example. 2) Ask your students to interpret the data. What can be learned from this image? The basics of bee plots. As expected, the 11-20, 21-30, and 31-40 groups ran the fastest. However, I think 31-40 was the slowest of the three groups, with a bit more variability.  3) I guess this would also be a good example of a non-linear ...

All of my favorite stats discussion topics in one place. Boom.

 I really hope this makes your online stats discussion boards less painful. Just share interesting stuff with your students and bring them around to loving stats like you do. Get them thinking like statisticians and see how data can inform all manner of life domains. For each idea, I share the original blog post related to the discussion, the topics covered, and the prompts I use in my online class. Enjoy! They are here:  https://docs.google.com/document/d/15rEG6h28xEeFrd8k4s1970BTZE153iR7/edit?usp=sharing&ouid=103379104980266607732&rtpof=true&sd=true

Andi Putt's infographics on autism prevalence demonstrate y-axis truncation and the surveillance effect.

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...

This example starts with a chi-square but ends with a lesson on how even well-written prompts can result in hallucinations.

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...

Use spicy, spicy peppers to explain scales of measurement and/or the difference between categorical and continuous data.

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 ...