Spot on with the alchemy remark, I've made similar comparisons before. Coming into bioinformatics/computational biology with a strong discrete math background I found a lot of professors excited to work with me until I started telling them how their ideas and models and experiments didn't imply what they wanted them to. Just like the startup world is awash with "it's like Uber for X" the biology world is full of "let's apply X mathematical technique to $MY_NICHE" and somehow this is supposed to always generate novel positive results worthy of publication. Then you tell them that you applied such-and-such mathematical/statistical model to their pet system and that the results contradict their last 10 years of published papers . . . and they ask you to do it again.

I remember one professor studied metabolic reaction networks modeled with differential equations. The networks themselves were oversimplifications and relied on ~5N parameters (N being the number of compounds in the network). The problem was that while all the examples in publication converged on a nice steady state (yay homeostasis is a consequence of the model!) it was trivial to randomize the parameters within their bounds of experimental measurement and create chaotic systems. Did this mean the model wasn't so great? No, it just meant those couldn't be the real-life configuration of those parameters . . . sigh. And now I'm a data engineer and no one asks me to get data from an API that doesn't actually provide it and I'm much happier.