Have you ever looked through stock images for data and statistics? They are fine. They aren't great, they aren't awful, they are just fine. Like, they either look like something from The Matrix: Or like statisticians are magicians: Some images are a combination of the first two, with a statistician-magician freeing himself from The Matrix: Again, they aren't terrible but they aren't beautiful.  Lucky for you, I'm a magpie collecting interesting statistical things, including interesting statistics/data images accompanying news stories. Many talented artists have created beautiful, abstract stats images to accompany their articles. They look very nice as a background on a PowerPoint, an opening page on Blackboard, or an image on a syllabus. Here are some of my favorites. I've included citations or where I found the images where I can and plan to reverse Google search the rest someday. Needless to say, I created none of this.  https://twitter.com/RetractionWatch/st...

My friends, winter is coming. Winter in Erie, PA, is no joke, so I've been encouraging my kids to pick up inside hobbies. My youngest is all about flipbooks right now, which inspired me to create my own statsy flipbook: Which, in turn, inspired me to create a blog post about statsy crafts. Crafts that you can do over Winter break for fun or maybe use as assignments for your students? A DIY Christmas gift for your favorite statistician?  The flipbook idea is an easy one to implement, as you only need index cards, a binder clip, and a pencil. Actually, many these can be done on the cheap if you have Legos, paper and pen, a log, yarn, baking supplies around. Not free, but not too expensive, either.  Data visualization via knitting A knitting-data-visualizer tracked temperatures via a knitting project, seen below. The different colors of yarn represent different temperatures on different days. Here is a full article from Gizmodo , which includes a link where you can purchase suppl...

 I've been a fan of Chalabi's work for years (here is my favorite example of mean vs. median). She makes beautiful, hand-drawn data visualizations . She created a beautiful mural that represents New Yorkers. And when I say "represents," I mean that this image is a representative sample of New Yorkers. https://monachalabi.com/product/100-new-yorkers/ Her sample of 100 New Yorkers was not drawn (Drawn? Get it?) at random.  Below, in her own words, Chalabi describes her work and what it means: https://www.absolutart.com/us/artist/mona-chalabi/artwork/100-new-yorkers-ii/ This is a novel way to talk about sampling, and representative samples, weighing survey response options to make a sample more representative, etc. You could even get into sampling error and other problems created by non-representative samples. 

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 ...