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:

Dr. Morton Anne Gernsbacher has freely shared her WHOLE Psychological Statistics class with the world. No paywalls, no log-ins.  Divided up sensibly, including sections on effect sizes and Bayesian. With some assistance from Chelsea Andrews, she created this phenomenal resource for all of us to use. While I'm focusing on her stats class, be sure to check out her amazing Research Methods and Psychological Effects of the Internet courses. DO NOTE: I'm not primarily recommending this directly as a resource as a class for students. I'm recommending it to you, instructors, as a source of great, free statistical readings and teaching ideas that you could incorporate into your own classes. This is especially helpful if you are trying to teach without a textbook or if you are just looking for additional ways of explaining tough statistical concepts to your students. Here are a few things she shares that you could use: 1. Plenty of guidance for teaching via Excel/Google Sheets/App...

I've blogged about Our World in Data before. There is a lot to appreciate at this webiste, but I would like to draw your attention to the wealth of interactive COVID visualizations you can create. Many of these visualizations include a toggle button that changes the graph from a logarithmic graph to a linear graph. Which really, really helps illustrate log data transformations to our novice statisticians. There have been occasional dust-ups over the last year with people not understanding the difference  or  being unfamiliar with log transformations , or graphs not being appropriately labeled.  I also like this example because most of my examples skew towards American content, but this data visualization tool lets you select from many countries .  ANDPLUSALSO: There is data to be had at the website. Data for days!

 I found this Tweet, which leads to a brief report on BBC. A recent report from the World Obesity Federation shows COVID death rates are higher in countries where more than half the population is overweight. Cause and effect, or bad statistics? @TimHarford and @d_spiegel explore - with some maths from me. You can listen on @BBCSounds https://t.co/hevepmz8RC — stuart mcdonald (@ActuaryByDay) March 14, 2021 The BBC has a show called "More or Less," and they explained a recent research finding connecting obesity to COVID 19 deaths.  Here is the original research study . Here is a pop treatment of the original study . For more stats news, you can follow  "More or Less" on Twitter . And they cram, like, a half dozen lessons in this story. It is amazing. I've tried to highlight some of the topics touched upon in this story. How can you use it in class? I think it would be a good final exam question. You could have your students listen to the story, and highlight ...

Go follow Statsystem ( Facebook , Instagram ). They are a smarty pants who dreams and thinks in stats memes. I know these are silly and hilarious and fun. I think these are good for a nerd chuckle for ourselves. And who doesn't need more nerd chuckles? I also think these are light, funny, accessible ways to back up a more complex stats lesson with a succinct meme that conveys the guts of a lesson.  See: Power Regression: Central Limit Theorem: T-tests: And don't forget the big picture:

This teaching example: 1. Is psychology research. 2. Features the actual data from the generous and helpful Dr. Bray . 3. Features GIFs. EVERYTHING is better with GIFs. 4. Includes puppies. 5. Includes a good ol' Psych Statistics standard: The one-sample t-test. Okay, get ready. I first learned about Dr. Emily Bray's dog cognition research via Twitter . Never let it be said that good things don't happen on Twitter. Occasionally.  1 Dogs are known for their ability to cooperate with humans and read our social cues. But are these skills biologically prepared? To find out, we tested 375 puppies at 8.5 weeks on 4 social cognition tasks (task descriptions: https://t.co/aETequNBce ) #AnimBehav2021 #Cognition pic.twitter.com/7vN2lp82Dp — Emily Bray (@DrEmilyBray) January 27, 2021 This is such a helpful way to share your research. This example works for your Cognitive or RM classes as well as your stats class, since this thread illustrates not just her findings but her methods. T...

When I teach inferential statistics, I think it is helpful in providing several conceptual (no by hand calculations, no data analyzed via computer) examples of experiments that could be analyzed using each inferential test. I also think it is essential to use non-psychology examples and psychology examples because students need to see how stats apply outside of psychology. At times, I believe that students are convinced that a class called Psychological Statistics doesn't apply outside of psychology.  So I like this quick, easy-to-follow example from medicine. Thomas, Patel, and Bittel (2021) studied how different vitamin supplements affected outcomes for people with COVID-19. The factor (COVID intervention) has four levels (usual care/control, ascorbic acid, zinc gluconate, and ascorbic acid/zinc gluconate). And the four groups acted pretty much the same. Bonus stats content: Error bars, super-cool Visual Summary of a research study that really highlights the most essential parts...