Twitter is my muse. This blog post was inspired by this Tweet:    In a study comparing blood concentrations of caffeine after coffee or energy drink consumption, blood caffeine levels peaked at about 60 minutes in all conditions. Plan accordingly. https://t.co/cWWakGGtHe pic.twitter.com/c5Nn3x3w1f — Kevin Bass (@kevinnbass) November 30, 2021 This study is straightforward to follow. I, personally, think it is psych-friendly because it is about how a drug affects the body. However, it doesn't require much psych theory knowledge to follow this example. Sometimes I'm worried that when we try too many theory-heavy examples in stats class, we're muddying the waters by expecting too much from baby statisticians who are also baby psychologists. Anyway. Here are some things you can draw out of this example: 1. Factors and levels in ANOVA The factor and levels are easy to identify for students. They can also relate to these examples. I wonder if they used Bang energy drinks? They a...

Flowingdata is great. They create lots of exciting data visualizations and share other people's visualizations.  This visualization from Flowingdata is especially significant.   I think it illustrates base rate fallacies beautifully. Moreover, it is applied to a very crucial issue: Immunizations. The base rate fallacy has been used repeatedly to attack the efficacy of vaccines . In particular, instances when vaccinated people catch diseases for which they have been vaccinated. Frequently, such arguments fail to consider base rate data regarding how many more people are vaccinated.  This illustration from Marc Rummy is elegant and straightforward and explains a mathy/sampling/statsy concept without any actual math. I love it.  Also, this illustration has been updated recently with a bit more text to explain everything: Apparently this picture I made that was part of a post 4 months ago recently went viral. Here's a new & improved version that includes the explana...

 OK. I'm proud of this one. I think these are good chi-square examples. And they are about Americans' beliefs about various supernatural beings. Presented with commentary because I couldn't help myself. 1. Supernatural beliefs by age Already. I fully intended to tease my traditional UGs about their beliefs about vampires. Because I do believe that would probably be a significant chi-square...but... ...IS GEN X OK? This survey asks about ghosts, demons, psychics, vampires, and werewolves. What are the "OTHER" this survey is talking about? Aliens? Dolly Parton? I'm intrigued. 2. Werewolves: The Unity Horse. The Unity Wolf? Both Trump and Biden supporters are united in their belief that werewolves do NOT exist.  Anyway. This survey is intriguing. There is a lot of material to work with.  Go read it here .  PS: Hey! If you like this idea and would love a whole stats textbook from the brain of the person who came up with this idea, sign up for more information abou...

There are plenty of stats calculators all over the interwebz. Power calculators, calculators for every test statistic under the sun, etc. BUT: Omni Calculators   I would try to list all of the different calculators, but I just don't have that sort of time on my hands. Descriptives? Sure. Inferential? Why not. Risk? Certainly. For more on the company that has created this calculator, read up on Omni Calculator here . ALSO: There are plenty of non-stats calculators available on the website, with a total of 2,122. 

This blog post was inspired by the brilliant Leslie Berntsen. Her ACT 2021 presentation,  " I'm not a therapist: Mental health education and advocacy for non-clinicians," was about teaching and sharing sound practices in mental health care when you, yourself, are not a mental-health-care-type-psychologist.  Anyway, she had this really great idea about sharing user/trends/etc. data from the Crisis Text Line  (CTL) with students. CTL is a text-based mental health crisis hotline. It is staffed 24/7. I include it on my syllabus as a resource for my struggling students because, unlike my uni's very hard-working counseling center, CTL is always available.  https://www.crisistextline.org/ Why share data from CTL in your stats class? Because you can use it to a) teach a bit of stats, b) introduce students who need this resource to the resource, c) introduce potential volunteers to the service. Here are my ideas for how you could use this data in class. 1. Data visuali...

As I write this blog post, Francis Haugen testifies on Capitol Hill and sheds light on some of Facebook's shady practices. TL;DR- Facebook realizes that its practices are support terrorism.  This led to a public relations blitz from Facebook, including Monika Bickert, who appeared on CNN . Of particular relevance is repeated reference made to an Instagram survey of 40 teens ( here is the documentation I was able to find ). I saw this tweet from Asha Rangappa Reaction about one of those interviews: https://twitter.com/AshaRangappa_/status/1445487820580081674 LOL I had to listen to this twice to make sure I didn’t mishear: This @Facebook exec repeatedly refers to a “survey” of FORTY teen Insta users— as in 4-0 — to support her assertion that the “majority” of teens have a great experience on the platform. For real. Listen to it https://t.co/Ye7ocWcnzG — Asha Rangappa (@AshaRangappa_) October 5, 2021 This example packs a lot of punch. It is a good one for the youths because it is ...

Maybe this is the sort of thing you will like. I guarantee you, it is spook-tacular.   https://docs.google.com/presentation/d/1Gu4VYsYTNJ28RtBf6cNp1xszjo-bfItSHAp-C0PiEbY/edit?usp=sharing

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...