My favorite way to illustrate the difference between expected and observed values is to use US Senate data on gender distribution. Why this data? There are 100 senators, two from each state. Gender is randomly distributed. So, comparing the observed data (the actual number of women senators) to the expected value of 50 senators is a pretty intuitive, easy-to-follow example. I then immediately discuss the same O vs. E discrepancy with the House of Representatives data, which doesn't start on a nice, neat scale of 100. I have to update this every two years to stay current. If you use this example, this might be a good time to update your own lecture. I use data and visualizations from Rutergers Center for American Women and Politics . The purple bar represents the total number of women in the Senate and House of Representatives as of January 2027.

Agree to Disagree is a regular feature on LADBible.  This series pairs up two celebrities who are typically on a promotional tour for a new movie or show. The interviewer gives them a series of questions, and they need to use a Likert-type scale printed on a table and glasses (sometimes containing booze) to make their response. For example, here is an Agree to Disagree segment featuring Keira Knightley and Ben Whishshaw , who are promoting the second season of Black Doves on Netflix. PS: I LOVED THE FIRST SEASON OF BLACK DOVES. So, it starts with a question: Then the "participants" give their response on a Likert-type scale.  Then they discuss their responses. PS: Keira swears like a sailor. How to use in class: 1) This might be the only entertaining way ever to introduce Likert-type scales. ALSO: If you really want to nerd it up, you can have a class discussion about scales with   no neutral point . 2) I also like this as an example of a mixed-methods survey. The pe...

These screenshots come from the Netflix adaptation of the book The Love Hypothesis . And they are the cheesiest way ever to describe Type I and Type II errors.  1. Screenshots courtesy of my pal Dr. Jordan Wagge. 2. I dramatically read this interaction to my class and recommend that you do the same. If this idea was helpful, you'll probably enjoy my textbook, Psychological Statistics for Everyone . It's basically this blog, but organized into a semester.

Statistical literacy is important. I love examples of folks using statistical concepts without being snobby statistical gatekeepers.  For instance, see jargon-free statistical literacy that helps people make important decisions about their children's health or informs the  immigration debate .   As stats teachers, I think we are remiss if we don't talk about political polling in our classes. At least a little bit. Here is a brief, nonpartisan interview about political polling you can use in class to see whether students can identify the concepts they've learned. Here is a short  NPR i nterview o n  how to think properly about political polling. If you use the interview in class, you could ask your students to identify the statistics and research methods mentioned.  I clocked margin of error,  sampling error,  and research methodology evaluation. If using an audio interview isn't your thing, check out t his summary piece from NPR, based on the...

The thing about my way of teaching stats is that my cool, novel examples get old really fast. Like, pop culture is on a thirty-day cycle. Anyway, YEARS ago, 2014 to be exact, I blogged about illustrating bimodal data distributions  using baby name data from the Social Security Administration. Specifically, when an older name comes back into vogue, it has a bimodal distribution when you plot time on the X axis.  I stand by this example. However, all of the data illustrations come from 2014. When our traditionally aged college students were 6 or 7. Which makes this example feel a little stale. However, here is some data,  courtesy of Redditor  fevaah ,  showing  old names that are back in vogue through 2025 ! And Eleanor, Violet, Nora, and Hazel are here, illustrating bimodal distributions for a new generation. If you used my old baby-name data, this is a quick, easy refresh for your lectures (or a fun new addition if you didn't).  If this idea was help...

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My textbook has a feature in every chapter: Dirty Data Alerts. It warns students about ways that data can be manipulated without straight-up lying. I have one Dirty Data Alert dedicated to manipulated  y- axes . Like, truncated y -axes. Because that is the way you manipulate a y -axis, right? RIGHT?! Oh, no, there is a new way to manipulate them without lying. Check out this example, pointed out by @SarahStarberry. Here, the y-axis measures the version number. That's all. Not how good the tool is, or how often it is being used. Just the version number. Like...just wait until I come out with JessChattyTalkTalkBot 8.0 and I destroy all existing competition. If this idea was helpful, you'll probably enjoy my textbook, Psychological Statistics for Everyone . It's basically this blog, but organized into a semester.

This post isn't a specific lesson idea, but a resource that will delight your statsy brain. We Have The Data on Instagram  ( and elsewhere ) is an account that shares interesting data, data in a variety of formats and from a variety of sources about all sorts of topics. For example: This is anti-brain rot for your IG feed. I also think there are countless classroom examples embedded in the content they share, especially as this account shares specific data points that are data that don't feel like data (like the difference in what 10.2 million will get you in two different parts of the country). I think they could be used in class to get your students to think more like a statistician. Enjoy.  If this idea was helpful, you'll probably enjoy my textbook, Psychological Statistics for Everyone . It's basically this blog, but organized into a semester.

Sometimes, an example is so little that I forget to add it to my blog. Like this, my favorite way to review scales of measurement and ordinal data in particular.      If this idea was helpful, you'll probably enjoy my textbook, Psychological Statistics for Everyone . It's basically this blog, but organized into a semester.

1) Not every example needs to be big and fancy.  2) Sometimes you just need to refresh a lecture. By "refresh", I mean both new-to-you examples and examples that are, objectively, new. That is why I'm adding this example from the New York Times  to my Fall 2026 classes. It was published in the summer of 2026, based on 2025 data that explores both the overall popularity of emojis AND generational differences in emoji usage. How to use in class: 1) A lesson in repurposing existing data sets: 2) Ordinal/rank order data: Here are the top ten emojis for 2025, as well as their change in rank. The rise of the "loudly crying face" is concerning! If a top ten list exists...then certainly a bottom 10 list exists. See:  Please see below for emojis 490-500. Poor bread. You can also discuss rank data with this list of fastest rising emojis. Or you can use this example of emojis that are increasing in popularity, with a dash of relative percentages: 3) They also looked at emo...

IG users  antiart  et al. (2026) inadvertently created a great example of a non-significant correlation and/or regression for you to use this fall when they created a scatter plot that illustrates a celebrity's nepo baby (or old money) privilege vs. their career accomplishments. https://www.instagram.com/p/DXKWBlHCicc/ It is funny, right, but it does get to the guts of linear relationships. Some nepo babies are talented, like Miley Cyrus. Some have not (poor Chet Hanks). You also have folks who have done much despite humble beginnings, like Eminem and Obama. Hence, non-significance/non-predictive linear relationship. Also, sometimes I think stats teachers (myself included) lean too far towards only sharing statistically significant/medium-to-large effect sizes, but they need to see all types of results.   Also, I can't identify half of these people, but I bet your students can.  If this idea was helpful, you'll probably enjoy my textbook, Psychological Statistic...

Ryosuke Fujii's (2026)  How to Design Effective Scientific Figures  lives up to its title and is written at a level accessible to undergraduate and graduate students when conference time rolls around.  It was published in Nature, and it focuses on understanding which type of figure to use depending on a) your data and b) your audience.  It is only three pages long, and I found myself nodding and agreeing with the main points, summarized here: Additionally, the article contains this helpful flowchart detailing when to use which graphs. I appreciate that the flowchart isn't jargon-heavy and that it focuses on functionality.  

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? See: Don't worry, I made a whole PowerPoint to explain . And maybe you can use the PowerPoint the next time you teach SEM. If this idea was helpful, you'll probably enjoy my textbook, Psychological Statistics for Everyone . It's basically this blog, but organized into a semester.

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 of 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 y...

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...