Not awful and boring ideas for teaching statistics

1) Not every example needs to be big and fancy. 2) Sometimes you just need to refresh a lecture.

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 c…

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.

I really, really believe in using non-scientific examples and comparisons to teach statistics.

I intended for this to be a post about the singer vocabulary.

Today, I'm taking a break from blogging about college teaching and sharing a mini-lesson I created for elementary school-aged students.

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.

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.

TL;DR: Birds fly away from men a bit sooner than they fly away from women.

Two things I love to use when teaching stats are: 1) Journal of the American Medical Association (JAMA) visual abstracts.

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.)

Two of my very favorite statistics comic sources are Saturday Morning Breakfast Cereal and XKCD.

This is just a little one-way ANOVA with three levels.

You guys.

Hey, I have kids, so I knew that Mr.

I really hope this makes your online stats discussion boards less painful.

This example illustrates how better assessment has likely increased in autism diagnoses (as opposed to the increase being due to vaccines or hysterical parents).

A research study counted how often ChatGPT made up citations for three different categories of mental disorders (binge eating, body dysmorphic, and major depressive).

This spicy example explains scales of measurement, continuous vs. categorical variables, and how you can measure and quantify anything.

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).

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.

No, that is not a dig at her romantic relationships.

Goodhart's Law is a truism in assessment circles, which are always statistics-adjacent.

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.

This blog post contains one small analogy for explaining family-wise error to your students.