You know how every few months, someone Tweets about stock photos that are generated when you Google "professor"? And those photos mainly depict white dudes? See below. Say "hi" to Former President and former law school professor Obama, coming it at #10, several slots after "novelty kid professor in lab coat". Well, Pew Research decided to quantify this perennial Tweet, and expand it far beyond academia. They used Machine Learning to search through over 10K images depicting 105 occupations and test whether or not the images showed gender bias.  How you can use this research in your RM class: 1. There are multiple ways to quantify and operationalize your variables . There are different ways to measure phenomena. If you read through the report, you will learn that Pew both a) compared actual gender ratios to the gender ratios they found in the pictures and b) counted how long it took until a search result returned the picture of a woman for a given j...

Here is another example of why representative sampling MUST include women. For years and years, car crash test dummies for adults were all based upon the 50th percentile male. As such, even in vehicles with high safety ratings, women still have higher rates of certain injuries (head, neck, pelvis) than men. In fact, the article cites research that found that belted female car occupants in accidents have a 47% higher chance of suffering a serious injury and a 71% higher chance of suffering a moderate injury compared to men in a car. http://leevinsel.com/blog/2013/12/30/why-carmakers-always-insisted-on-male-crash-test-dummies I wrote a previous blog post about this video that outlines how using only  male rats for pharmaceutical research lead to problems with disproportionately high numbers of side effects in female humans . And this NPR story details changes to federal rules in order to correct this issue with animal testing. How to use in class: -Inappropriate sampling i...

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...