Interpreting effect sizes requires more than Rules of Thumb for interpretation. Interpretation requires deeper knowledge about the investigated topic, an idea we must convey to our students. For example, in presidential elections in the United States, the winner is usually selected by a slim margin. As such, if you can get even small numbers of voters who don't usually vote to vote, it can have a large real-world effect on an election. This is what Vote Forward is trying to do, and I'll explain how you can use their work to explain effect sizes in your stats classes.  This is Vote Foreward : Okay. So they are organizing letter-writing campaigns in advance of the 2020 General Election. NOTE: The organization is left-leaning, but many of its campaigns ask letter-writers to share non-partisan messages.  Vote Forward has tested whether or not writing letters to unlikely voters actually gets people to vote, and they shared the results of those efforts: Their findings, which aren...

 Hi, While Blogger does allow me to tag my posts, I thought it might be easier if I just created a compendium for the major sections of Psych Stats? Especially since the search function doesn't work great on mobile devices. And sometimes, you don't want to go poking around and just need to prep for a class fast.  Also, every blessed one of you deserves an Easy Button here in the middle of a pandemic.  And, of course, my mind organizes the world into spreadsheets, so I made a spreadsheet. I hope this helps with your teaching. https://docs.google.com/spreadsheets/d/1b_FcZkJKf5a5M05Jwp62ZJiVYu6s51W2WXve4L8r1MU/edit?usp=sharing PS: Be on the lookout, I'll probably do this for ANOVA, chi-square, regression, correlation, etc.

Time to make this video of Dr. Fauci testifying before Congress Not Awful and Boring Cannon. Because it is beautiful to behold. Here, Dr. Fauci drops truth bombs at a congressional hearing about COVID-19 research. In it, he critiques a hydroxychloroquine research study for not having a control group, having confounds, no randomization, and he talks smack about peer review. And the most important thing? He states that he would change his mind about hydroxychloroquine if compelling data from a well-designed study indicated that he should do so. Because science changes when the evidence changes.  

The Economist created interactive visualizations for various polling data related to the 2020 U.S. Presidential election.  While illustrating this data, they used different measures of central tendency and different confidence intervals. Like, it is one thing to say that Candidate A is polling at 47% with a margin of error of 3.2%. I think it is much more useful to illustrate what the CI is telling us about the likely true parameter, based on what we have collected from our imperfect sample. The overlap in confidence intervals when polling is essential to understanding polling.  How to use in class: 1) Electoral college predictions, illustrated with median, 60%, and 95% confidence intervals. Also, I like how this illustrates the trade-off between precision and the size of a confidence interval. The 60% CI is more narrow, but you are only 60% confident that it contains the true number of electoral college votes. Meanwhile, the 95% confidence interval is much wide but also more ...

This is an engaging example of mode.  Barbara Thompson, writing for NetCredit, shared a report on the most common last names in every country, beautifully summarized via poster . The color coding represents the origins of the last names. I stared at this map for a very long time when I first saw it. NetCredit also shared its data via Google Sheet . How to use in class:  1) Mode example. 2) Ask your students how they think this data was calculated, then send them to the webpage to learn more about how the data was calculated.  3) Go to the full article for this data. They break the data up, continent by continent, to explain how the modal names came to be due to naming tradition/history in each country/region/continent. I'm a big nerd and think that sort of thing is fascinating. 

This is a beautiful story from Reddit, with a very kind Redditor, Higgnenbottoms/Quoc Tran, who shared his data with all of us, so we can use this as an example of a) independent t-tests, b) violin plots, AND R.  So, user r/quoctran98  wanted to know if Daves knew more Daves than non-Daves do. HA! He started by collecting data from r/samplesize .  Do you all know about that subreddit, where you can post a survey and see who responds? You're welcome. Anyway, Quoc analyzed his data AND created a violin plot to illustrate his data. He shared it at r/dataisbeautiful , which is another excellent stats subreddit. See below. AND...here is the kicker...I contacted Quoc, and he shared his data (so your students can run their t-tests) AND his R code . I cleaned up his data a bit to provide the same results as the graph above (he had someone report that they knew 69 Daves. I mean, he collected the data from Reddit users.).

"The answer is always more spreadsheets." -Michael Hobbes The good news: 1) This isn't a COVID example. 2) This is one of those examples that gets your students to think more abstractly about some of the tougher, fundamental questions in statistics. Precisely: How do we count things in the very, very messy real world? What are the ramifications of miscounting messy things? 3) The example comes in the form of the very engaging podcast You're Wrong About , hosted by Michael Hobbes and Sarah Marshall. @yourewrongabout The bad news: The example is about human trafficking, so not nearly as fluffy as my hotdog or seagull posts. That said, this episode of the You're Wrong About podcast, or even just the first 10 minutes of the episode, reveals how hard it can be to count and operationalize a variable that seems pretty clear cut: The number of children who are trafficked in America every year.  The You're Wrong About podcast takes misunderstood, widely reported event...