The concept of a multilevel model, also called a mixed effects model or a hierarchical model, is reasonably new to me. It's not the kind of thing typically taught in physics (where there are very explicit models) or in machine learning, but is quite common in social science. I first came across it through Lauren Kennedy on the Learning Bayesian Statistics Podcast, through talking with a trained neuroscientist and a trained statistician who were talking about fixed and variable effects as I went cross-eyed, and through the excellent Regression and Other Stories textbook which makes many allusions to it (to be expounded on in their upcoming sequel Applied Regression and Multilevel Models).