I could really relate to this description of learning how to use statistics in research from my own experience as a PhD student. I remember presenting the results of a repeated measures ANOVA and somebody told me I had to use Mauchly’s sphericity test first, he wasn’t really sure what it did, but said reviewers would always ask to see the results from it… (I recall looking into it a bit, but I don’t think I ever really figured out what it did either!)
Learning statistics on the job as a junior researcher is a bit like being inducted into a secret society. Nobody expects you to fully understand the papers peppered with mathematical notation that explain how a certain statistical technique works. Instead, people understand in practice , to a better or worse extent, how to apply it, even if they don’t have that clear a picture of what they’re actually doing on a deep level. As a new graduate student, older ones might give you pointers on which golems to use, while freely admitting to having only a very superficial understanding of them, which means that troubleshooting any problems that arise is near impossible. Fitting models to and making inferences from your data become slightly alchemical processes, with everyone having their own approach often based on a somewhat ramshackle grasp of the concepts involved.