This video provides an example of mode when it reveals survey data about the most common for of birth control used by married women or women who live with their partners. Before revealing the answer, they have strangers sitting in a produce department of a grocery store discussing their best guess for the answer? Huh? Well, at least you get to listen to strangers awkwardly talking about pulling out in front of a bunch of vegetables. I think that traditionally aged college students are a little surprised by the modal response: Sterilization. This opens up the opportunity to talk about the sampling: They could only survey women who are electing to use birth control (so, not trying to get pregnant) AND in a long term relationship, so a more permanent form of family planning is probably more attractive.

I think this is a good first day of class example. It shows how data can make a powerful argument, that argument can be persuasively illustrated via data visualization, AND, maybe, it is a soft sell of a way to keep your students from drunk driving. It also touches on issues of public health, criminal justice, and health psychology. This article from The Economist  succinctly illustrates the decrease in drunk driving incidents over time using graphs. This article is based on a  working paper  by PhD student Jessica Lynn (name twin!) Peck. https://cdn.static-economist.com/sites/default/files/imagecache/640-width/20170408_WOC328_2.png Also, maybe your students could brainstorm third variables that could explain the change. Also, New Yorkers: What's the deal with Staten Island? Did they outlaw Uber? Love drunk driving? 

UPDATE: 9/22: Sex ratio in India is normalizing: https://www.pewresearch.org/religion/2022/08/23/indias-sex-ratio-at-birth-begins-to-normalize/ I use this story from The Economist as a conceptual explanation of the one-sample t-test.  TL:DR: Sex ratio disparity data out of India is an abstract introduction to the one-sample t -test. So, at its most basic, one sample t -test uses some given, presumably true number/mu and tests your sample against that number. This conceptual example illustrates this via the naturally occurring sex ratio in humans (your mu) versus 2006-8 sex ratio data from different states in India (your sample data). Why look at this data? Social pressure, like dowries, high rates of sexual violence against women in India, etc., make male offspring more attractive than female offspring to some families. And the data provides evidence that this is leading to disturbing demographic shifts. For example, see the table below from The Economist: http://www.ec...

TED isn't the only source of videos for teaching statistics . The Economist also makes animated videos that are lousy with data. One easy, no-pay-wall source for such videos is The Economists Videographic playlist on YouTube  (there is a limit on number article views/month at their website ). One really statsy video from The Economist that I've featured previously on this blog explains the real life implications for Type I/Type II error in research (and, specifically, how it leads to errors in published research ). The other videos may not be as directly related to the teaching of statistical topics, but they do illustrate data. Topics range from American union membership trends to this video about world population growth . As you may have inferred from the source, many of these videos focus on national and global economic information, but all of the videos do present data that you can integrate into your classes. Some are more applicable to teaching statistics: This vid...

A great, foreboding video  (here is a link to the same video at YouTube in case you hit the paywall) about the actual size and implication of Type II errors in scientific research. This video does a great job of illustrating what p < .05 means in the context of thousands of experiments. Here is an article from The Economist on the same topic. From TheEconomist