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...

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

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 ...

 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

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...