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 ...

 Hi! If you want to hear what my actual voice sounds like as I talk about the teaching of statistics, check out this interview I did with Eric Landrum for the Psych Sessions podcast.

 https://today.yougov.com/topics/lifestyle/articles-reports/2021/05/13/lions-and-tigers-and-bears-what-animal-would-win-f Sorry that I haven't been posting as often lately. You would think that with the summer, I would have more flexibility, but I am working hard on some writing deadlines (for a stats textbook!), and my kids' activities have picked up considerably with soccer season starting. This example illustrates fun data visualizations as well as a t-test. YouGov is a polling company, sort of like Gallup. They collect many Very Serious polls and silly polls like  this one, where they asked participants to state whether or not they could beat 34 different animals (from rats to grizzly bears) in an unarmed fight. Their graphic designer deserves a raise for this bar graph, including several tragic humans vs. animal memes/movie clips. Here are a few lessons you can draw out of this funny data. Paired t-test example: They took the participants identified as men and women and...

It occurred to me that I haven't shared my absolute most precious professional development and stats teaching resource in the blog.  That resource is the Society for the Teaching of Psychology. Non-psychologists reading this post, don't stop now. Keep going.   1. There are multiple free e-books available from STP. Some are about teaching, broadly. Some of them have a chapter or two devoted to the teaching of statistics. But there is at least one exclusively devoted to the teaching of statistics,  For the Love of Teaching Undergraduate Statistics . I wrote a chapter in the book, so I'm partial.  2. The STP journal, Teaching of Statistics , includes pedagogy research about the teaching of statistics . Full disclosure: I'm a consulting editor at this journal. 3. If you are a member of STP and come up with an excellent teaching idea or study idea related to teaching statistics to psychology majors, STP has got some money for you . They have several grants, reviewed...

Hi! Here is all the material I shared at my keynote address at Teaching of Statistics in Psychology. I included three exercises, with slides, you could use to teach confidence intervals, chi-square, and t-tests. Here is the talk: