Why is controlling for too many variables considered harmful? I am trying to understand the point of the second panel in the following xkcd comic:

Specifically, how can one be misled by controlling too many confounding variables in one's models?
Any pointers to what this criticism is called in the literature—so I can look into it further—will be appreciated.

Well, it's related to the concept of p-hacking. Given a sufficient number of plausible confounding variables to be added in the study, it's possible to find a combination of them that would yield significant results (you just plug in or out until you get significant results, and you report those).
There's a very nice post in FiveThirtyEight about this so you can experience the idea, where you can even obtain contradictory results depending on which variables you wish to "correct for".
