I really, really believe in using non-scientific examples and comparisons to teach statistics. In this post, I will show you how I see comparisons between criminal investigations and scientific investigations. Specifically, after a summer spent going down a true-crime rabbit hole, I think that criminal investigations and court cases are sort of like SEM. 1. Investigators and statisticians are sometimes forced to use tangible evidence to infer about intangible variables and concepts. 2. An evidence board is basically a path diagram, right? Don't worry, I made a whole PowerPoint to explain . And maybe you can use the PowerPoint the next time you teach SEM.

I intended for this to be a post about the singer vocabulary. It is still that, but it is also a post about using GenAI to grab data from an image. I mean, you can use Excel to do the same thing, but GenAI is a lot easier. Here we go. It starts with the Word Tips website, which helps you solve your crossword puzzles and Wordle. This website also has a blog dedicated to words. One such blog post explored which singers have the largest vocabularies, as measured by the number of  unique words in their lyrics. Their blog post compared music legends to newer talent. There are a ton of fun data visualizations on the website; go check it out. Since I teach college students, I decided to concentrate on the musicians my students listen to: In and of itself, this image serves as an example of bar graphs, good data visualization, and proper use of "buckets". However, I figured we could find a way to use the raw data in class. Create your own data visualization, create your own buckets....

Today, I'm taking a break from blogging about college teaching and sharing a mini-lesson I created for elementary school-aged students. I am sharing my activity here because I bet I'm not the only college instructor who has been asked to do community outreach with kids, be it at local STEM festivals, children's museums, or elementary school career exploration days. Feel free to take this idea and run with it. I taught this lesson as part of the Wonder Time series at my local children's museum,  Erie Children's Museum  in Erie, PA. My friend, Claire, is a former stats student and former STEM teacher at my kids' school. She is the current education director at the museum. PS: I love my small town life, and the museum is just a few blocks from my workplace.  A description of the Wonder Time series  My question was, "How do number experts predict the future?" As I put together my lesson, I was inspired by all the different ways instructors are already usin...

 Now and again, I run across a news article or psychological question that is so big that it bleeds out of straight statistics and requires a thorough understanding of the research methodology that guides statistical choices. When that happens, I email my buddy and fellow W.W. Norton author, Beth Morling, and we write a joint blog post. Recently, I emailed her because research on using psychedelics to treat many different mental disorders has been in the news.  President Trump fast-tracked this research,  and the  Journal for the American Medical Association recently published a big meta-analysis  on the topic. Psychedelic research has always interested me because of psychology, but it has always amused me because of how you run a proper double-blind research study if your experimental participants KNOW that they are hallucinating and your control group participants know they are not?  This broader question offers a few great discussion options for you and ...

I came across a Reddit post  in which a user did a quick-and-dirty data collection of the ethnicities of the three top-billed actors in each of 100+ million USD-earning movies between 2022 and 2025. They then compared the data to US census data.    Regardless of how Reddit reacted, I saw this and decided that it would make a good example for explaining and performing a chi-square with expected proportions. I'm so fun at parties, guys. While the original sample was 228, I created an imitation sample ( n = 100) with the Hollywood data as the observed data. I used the US census demographic percentages as the expected proportions.  Here is my n = 100 imitation data, in JASP , .TXT , and a text file of the R code generated by JASP. AND PLUS ALSO: The OP in Reddit gave their quick-and-dirty research methodology for collecting data on the ethnic breakdown of the top-billed actors in very successful movies. I think you could challenge your RM students to consider how they ...

TL;DR: Birds fly away from men a bit sooner than they fly away from women. Full stop. Here is the  original article,  and here is a write-up from  Nautilus . I love bird research. I'll get into why below. For now, let me show you how to use this example to teach three different lessons in a stats class. 1. Independent t test example with a data set The researchers shared their data. The researchers didn't analyze this data with a t test. But they did share this data visualization that looks a whole lot like one: Damn, I love the new trend of the box/violin/jitter plot. FYI: Researcher gender/the IV is labeled "gender," and how far the birds were before they flew away/the DV is labeled "FID" (flight initiation distance). Also, I love this example because the data violate the assumption of equal variance and provide a case for discussing Welch's test. 2. Conceptual example for Factorial ANOVA This example pairs well with a  previous blog post  featuring ...

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...

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...

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.

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...

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.