What is the name of the fallacy that denies inductive generalization based on emphasizing exceptions?

Question

I am looking for the name of a logical fallacy that denies validity of induction based on an observed and repeated pattern (AKA statistics) by citing exceptions to the pattern which are outliers of small percentage. Just to clarify, I am well familiar with the problem of induction and am not discussing the validity of using induction. I'm just looking for the name of something that, to me, appears as a commonly (ab)used logical fallacy.

E.g. insurance companies use statistics not to establish universal truths but to guide their investment risk. So, based on observed patterns, they established that drivers below 25 years old are more risky. So if Joe Denialist comes and says that the criteria is invalid because he's 24.5 years old and has been driving for 7 years with not even a speeding ticket, that would be "anecdotal evidence". But if he goes a step further and perhaps collects some data that presents still an overwhelming minority of cases relative to the documented stats to invalidate the risk strategy, is there a name for that logical fallacy?

Answer 1

It might fall under slothful induction, the lesser known antonym of hasty generalization, but it is not exactly specific to citing exceptions:

"Slothful induction is a logical fallacy in which an inductive argument is denied its proper conclusion in spite of strong evidence. While skepticism is valuable, a slothful induction occurs when someone falls into pseudoskepticism and demands an unfairly high amount of evidence before accepting an idea. Often, slothful induction becomes a game of moving the goalposts."

Cherry picking, a.k.a suppressing evidence, is also close, but again not specific to focusing on exceptions:

"the act of pointing to individual cases or data that seem to confirm a particular position, while ignoring a significant portion of related cases or data that may contradict that position. It is a kind of fallacy of selective attention, the most common example of which is the confirmation bias."

Of course, disconfirmation bias, which seems closer here, is itself a case of confirmation bias:

"Writing about a study that seemed to undermine the deterrence effect, a death penalty proponent wrote, "The research didn't cover a long enough period of time", while an opponent's comment on the same study said, "No strong evidence to contradict the researchers has been presented". The results illustrated that people set higher standards of evidence for hypotheses that go against their current expectations. This effect, known as "disconfirmation bias", has been supported by other experiments."

Nitpicking, "the practice of meticulously searching for minor, even trivial errors in detail (often referred to as "nits" as well)", also comes to mind, as does nutpicking, "showcasing the nuttiest member(s) of a group ["nuts"] as the best representative(s) of that group". Perhaps, the good old not seeing the forest for the trees is the closest, although it is more of an idiom than a named fallacy.