Our department at the University of Houston has a paper that is accepted and will be presented at the 2026 conference of the American Educational Research Association (AERA) in Los Angeles. Without getting too deep into the purpose of the paper, while simultaneously aiming for the purpose of this writing, which is showcasing our use of novel methodology in action, we used machine learning ML algorithms to make our case. As a result, we needed to validate the machine learning outcomes. Traditionally this is done with an 80/20 holdout reservation of part of the data. Indeed, one of our critics asked us to use a holdout method in a particular way. However, as a data scientist, I am always championing the newest methodologies out there available in the R coding sphere (read as a linguistic sphere altogether). As I was looking out during the break for newest releases, I came across the work of Jia, Imai, and Li (2024/2025). Their methods turn the traditional 80/20 holdout and cross validati...