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...

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

This summer, the Psi Chi Journal of Psychology Research published  Rouse, Russel, and Campbell's Beyond the textbook: Psi Chi Journal articles in introductory psychology courses. It is a curated list of paywall-free Psi Chi articles, mostly with student co-authors, that are peer-reviewed and of an appropriate writing level and length to use in an Introduction to Psychology course. The authors provide the following information for each of the articles: In addition to being appropriate for Into Psych, these articles are also perfect for Intro Stats. In my classes, I emphasize the ability to read and write simple result sections. One way I would review this skill is by showing my students Results sections from published research and asking them to identify the test statistics, effect size, and other relevant information. This selection of articles features clear and concise results sections for t -tests, ANOVA, factorial ANOVA, regression, and correlation. I created a spreadsheet...

Does anyone else teach correlation and regression together at the end of the semester? Here is a treat for you: Updated data on Leonardo DiCaprio, his age, and his romantic partner's age when they started dating. A few years ago, there was a dust-up when a clever Redditor r/TrustLittleBrother realized that DiCaprio had never dated anyone over 25. I blogged about this when it happened. But the old data was from 2022. Inspired by this sleuthing,  I created a wee data set, including up-to-date information on his current relationship with Vittoria Ceretti, so your students can suss out the patterns that exist in this data.

One of my favorite upper-level elective courses to teach is Positive Psychology. I recently came across a comprehensive account of various facets of how positive psychology can be assessed in nations:  https://ourworldindata.org/happiness-and-life-satisfaction . Like, the website is just great. Below is an example of the data you can explore, in various formats, animation options, and you can download the data. It is great! From this website, I downloaded and compiled two data sets that capture GDP, Cantrill Ladder Score, and life span data for hella countries. You can perform a variety of significant and non-significant correlations and regressions using this data. Additionally, the countries are divided into six regions, allowing you to conduct some one-way ANOVAs with your students.  Here is the data, compiled by my awesome RA, Maddie:  https://docs.google.com/spreadsheets/d/129NQcPdFwZjyzZAJdX6odKC7KiFk_Q1Lqa-SD4kk5FQ/edit?usp=sharing

  The Science of Scare project, conducted by MoneySuperMarket.com, recorded heart rates in participants watching fifty horror movies to determine the scariest of scary movies. Below is a screenshot of the original variables and data for 12 of the 50 movies provided by MoneySuperMarket.com : https://www.moneysupermarket.com/broadband/features/science-of-scare/ Here is my version of the data in Excel format . It includes the original data plus four additional columns (so you can run more analyses on the data): -Year of Release -Rotten Tomato rating -Does this movie have a sequel (yes or no)? -Is this movie a sequel (yes or no)? Here are some ways you could use this in class: 1. Correlation : Rotten Tomato rating does not correlate with the overall scare score ( r = 0.13, p = 0.36).   2. Within-subject research design : Baseline, average, and maximum heart rates are reported for each film.   3. T-tests : The is a sequel/has a sequel data can be used to perform a ...

Sometimes, you just need a new, simple example for a homework question or a class warm-up.   I eyeballed and entered the   data here  ( r   = -.55). Enjoy. I use this little example to explain to use the regression formula to make a prediction. Here are my slides .

As a research-parent-nerd joke before Christmas, six doctors swallowed Lego heads and recorded how long it took for the Lego heads to pass. Why? As to inform parents about the lack of danger associated with your kid swallowing a tiny toy.  I encourage you to use it as a class example because it is short, it describes its research methodology very clearly, using a within-subject design, has a couple of means, standard deviations, and even a correlation. TL;DR: https://dontforgetthebubbles.com/dont-forget-the-lego/ In greater detail: Note the use of a within-subject design. They also operationalized their DV via the SHAT (Stool Hardness and Transit) scale. *Yeah. So here is the Bristol Stool Chart  mentioned in the above excerpt. Please don't click on the link if your are eating or have a sensitive stomach. Research outcomes, including mean and standard deviations: An example of a non-significant correlation, with the SHAT score on the y-...