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...

Organizations have frequently tried to shut down/manipulate data for their own ends. Big tobacco and lung cancer and addiction research . The National Football League and Chronic Traumatic Encephaly . And for the last 20 years, the National Rifle Association has successfully blocked funding for research investigating public safety and gun ownership. Essentially, the NRA has concentrated on eliminating funding at the CDC for research related to a better understanding of how guns hurt people. It started in 1996 with the Dickey Amendment and no one has been willing to fight to bring back funding. The APA wrote a piece on this in 2013 that summarizes the issue. In the wake of the shooting in Orlando, NPR did a story explaining how the American Medical Association is trying to change the rules governing gun research  and  the L.A. times published this column . I think this precedence is unfortunate from both sides of the gun debate. I grew up in rural Pennsylvania. I've...

I love research that is counterintuitive. It is interesting to me and makes a strong, memorable example for the classroom. That's why I'm recommending Carroll's piece  from the NYT. It questions the conventional wisdom that breakfast is the most important meal of the day. As Carroll details, there is a long standing and strong belief in nutrition research claiming that breakfast reduces obesity and leads to numerous healthy outcomes. But most nutrition research is correlational, not causal. AND there seems to be an echo-chamber effect, such that folks are miss-citing previous nutrition research to bring it in line with the breakfast research. Reasons to use this article as a discussion piece in your statistics or research methods course: -Highlights the difference between correlation and causation -Provides an easy to understand example of publication bias ("no breakfast = obesity" is considered a fact, studies that found the opposite were less likely to...

If you are familiar with financial and racial disparities that exist in the US, you can probably guess where this article is going based on its title. Kids in wealthy school districts do better in school than poor kids. Within each school district, white kids do better than African American and Latino kids. How did they get to this conclusion? For every school district in the US, the researchers used the Stanford Educational Data Archive to figure out 1) the median household income within each school district and 2) the grade level at which the students in each school district perform (based on federal test performance). This piece also provides multiple examples for use within the statistics classroom. Highly sensitive examples, but good examples none the less. -Most obviously, this data provides an easy-to-follow example of linear relationships and correlations. The SES:school performance relationship is fairly intuitive and easy to follow (see below) From the New Yor...