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...

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 ...

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...

In our stats classes, we need MANY examples to convey both theories behind and the computation of statistics. These examples should be memorable. Sometimes, they can make our students laugh, and sometimes they can be couched in research. They should always make our students think. In this spirit, I've collected three small examples from the Pew Research Center's  Race and Ethnicity  archive (I hope to update with more examples as time permits). I don't know if any data collection firm is above reproach, but Pew Research is pretty close. They are non-partisan, they share their research methodology, and they ask hard questions about ethnicity and race. If you use these examples in class, I think that it is crucial to present them within context: They illustrate statistical concepts, and they also demonstrate outcomes of racism.   1. "Most Blacks say someone has acted suspicious of them or as if they weren't smart" Lessons: Racism, ANOVA theory: between-group dif...

Katherine Baicker ,  Oeindrila Dube ,  Sendhil Mullainathan ,  Devin Pope,  and  Gus Wezerek created an interactive, data-driven piece for NYT . It provides a new perspective on how we should proceed with re-opening businesses during the COVID-19 pandemic. They argue that we must consider 1) how long people linger in different types of stores, 2) how often they visit these stores, 3) the square footage of the stores, and 4) the amount of human interaction/surface contact associated with how we shop at different stores.  How to use this in class:    1) Show your students how data can inform real-life problems. Or crises, like how to safely re-open stores during COVID-19. 2) Show your students how data can be used in creative ways to solve problems. The present argument uses cellphone location data. 3) Show your students data viz in real life: Here, scatterplots that really improve the #scicomm potential of this piece. 4) Show your students the rese...