You have to walk before you can run, right? The basics we teach in Psych Stats help our students walk, but they are not typical of published psychology research. It is difficult for Psych Stat instructors to find good examples of our analyses in recently published research (for an exception, check out Open Stats Lab ). A recent publication caught my eye because I love sending people mail ( scroll down to find my list of recommended, envelope-friendly surprises ).  Liu, P. J., Rim, S., Min, L., & Min, K. E. (2022). The surprise of reaching out: Appreciated more than we think. Journal of Personality and Social Psychology , No Pagination Specified-No Pagination Specified. https://doi.org/10.1037/pspi0000402 Spoiler alert: People love being surprised by mail. Like, more than the sender thinks the receiver will be surprised. I was delighted to discover that this interesting paper consists of multiple studies that use what we teach in Psych Stats. Check out this article s...

This excellent interactive tool provides good examples  of chi-square thinking and interactions.  For more commentary, this  write-up of Orth's researc h  is an easy read. Also, it is an example that doesn't involve politics or COVID. There is a time and place for both in the statistics classroom, but I've missed good, accessible data that doesn't have ANYTHING to do with divisive issues.  The data is from research that asked people how they watch TV: Streaming services, cable TV, binge-watching, etc. All sorts of information on how we watch TV, and with this easy-to-use website, you can create tables from this data. Tables that help you teach all kinds of ideas. You can turn chi-square goodness of fit for whether or not people binge their TV. Into a chi-square test of independence by investigating binging by age (categorized).  You can also change the data visualization so you can see the data in a very traditional chi-square table You can also use these c...

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...