A man after my own heart. This is of minimal educational value but maximal stats humor. David purchased a Pure Drink water bowl for his cat. He wanted to know if it actually resulted in his cat drinking more water.  This wee (hahahaha) little study could be used on the first day of class to demonstrate: 1) A hypothesis 2) Operationalized variables 3) Within-subject research design  4) p (HAHAHAHHA)-values 5) What a god damn stats nerd their instructor is 6) The power of data visualization

Okay. I know there are so many COVID examples out there, but this one is maybe a tiny bit amusing (it involves Berlin dance clubs). It also demonstrates a within-subject research design and ecological validity. It is also a very tiny example that is easy to understand and doesn't require students to understand any psychological theories. Yes, many of you are psychologists teaching statistics, but I think it is vital that we use various examples to ensure that at least one of them will stick for every student. Emma Hurt/NPR Anyway. Berlin has a famous dance club culture , which has been under tremendous financial strain due to COVID-19. Since winter is coming and outdoor options will no longer be possible, the government has sponsored a pilot project to study whether or not clubs can be opened safely if everyone at the club has tested negative for COVID-19. NPR reported on this applied, within-subject design study  (a three-minute-long news story you could use in class): In addition...

Nicole Gilbert Cote, Jared Schwartzer & Natasha Matos created a great website, The Accessible Toolbox,  filled with ideas for creating an accessible statistics class. In addition to advice, they offer A FREE (thanks, APS!) 3-D Tool Kit for teaching stats , which includes :

HEY. Here is a whole bunch of data, searchable via Google.                       https://datasetsearch.research.google.com/ h/t: Samy ! 

First, a pun: American athlete Athing Mu broke the American record for the 800m. I guess you could say...that Mu is anything but average!! HAHAAAHAHAHHA. https://twitter.com/Notawful/status/1409456926497423363 Anyway. It is late June 2021, and my Twitter feed is filled with amazing athletes qualifying for the Olympics. Athletes like Sydney McLaughlin. That picture was taken after McLaughlin a) qualified for the 2021 Olympics AND b) broke the 400m hurdle world record. Which is amazing.  Now, here is where I think we could explain effect size interpretation. How big was McLaughlin's lead over the previous record? From SpectrumNews1 McLaughlin broke the world record by less than a second. But she broke the world record so less than a second is a huge deal. Similarly, we may have Cohen's small-medium-large recommendations when interpreting effect sizes, but we always need to interpret an effect size within context. Does a small effect size finding explain more variance than any pre...

The Pudding  took a data-driven approach to test a popular hypothesis: Women's pockets are smaller than men's pockets.  Authors Diehem and Thomas sent research assistants to measure the pockets on men's and women's jeans. They even shared supplemental materials, like the exact form the RAs completed. https://pudding.cool/2018/08/pockets/assets/images/MeasurementGuide.pdf And they used fancy coding to figure out the exact dimensions of the jeans. Indeed, even when women are allowed pockets (I'm looking at you, dressmakers!), the pockets are still smaller than they are in men's jeans. They came to the following conclusion: Amen. Anyway, there are a few ways you can use this in the classroom: 1) Look at how they had a hypothesis, and they tested that hypothesis. Reasonably, they used multiple versions of the same kind of pants. If you check out their data, you can see all of the data points they collected about each type of jeans. They even provide supplemental mat...

One thing I like to emphasize to my students is that just because a scientist is using math and science and statistics, it doesn't mean they are unbiased. I usually describe how Sir RA Fisher love statistics, smoking, and white folks and, shock of shocks, produced data that supported both the safety of smoking and the soundness of eugenics.  For more on that: How Eugenics Shaped Statistics, by Clayton for Nautilus Magazine . And now I have another plug-and-play, easy-to-implement example of checking your bias in your research. http://journals.sagepub.com/doi/abs/10.1177/0098628320979879 The article makes a sound argument: While social justice/bias issues may be present in other psychology courses, they need to be addressed in our stats classses as well.  This journal article from Teaching of Psychology suggests that a lecture that highlights Samuel George Morton's "research" that investigated skull size and intelligence, as well as more modern examples of bias, leads ...