This article from The Economist, " Data from wearable devices are changing disease surveillance and medical research ," has a home in your stats or RM class. It describes how FitBits and Apple Watches can be used to collect baseline medical data for health research. I like it because it is very accessible but still goes into detail about specific research issues related to this kind of data: -How does one operationalize their outcome variable? Pulse, temperature, etc., as proxies for underlying problems. Changes in heart rates have predicted the onset of COVID and the flu.  -Big samples be good! One of the reasons this data works like it does is because it is harvested from a massive number of people using these devices.  -The article gives examples of well-designed experiments that use wearable technology. However, often with massive data collection via tech, the data drives the hypothesis, not the other way around. In our psychology classes, we discuss NHST and the proper w...

One thing I like to emphasize to my students is that just because a scientist is using math and science and statistics, it doesn't mean they are unbiased. I usually describe how Sir RA Fisher love statistics, smoking, and white folks and, shock of shocks, produced data that supported both the safety of smoking and the soundness of eugenics.  For more on that: How Eugenics Shaped Statistics, by Clayton for Nautilus Magazine . And now I have another plug-and-play, easy-to-implement example of checking your bias in your research. http://journals.sagepub.com/doi/abs/10.1177/0098628320979879 The article makes a sound argument: While social justice/bias issues may be present in other psychology courses, they need to be addressed in our stats classses as well.  This journal article from Teaching of Psychology suggests that a lecture that highlights Samuel George Morton's "research" that investigated skull size and intelligence, as well as more modern examples of bias, leads ...