In theoretical classes (for engineering), such as on random processes or statistical learning theory, sometimes great emphasis is put on measurability. For instance, I have heard somewhat extensive explanations of Borel $\sigma$-algebras or the Vitali set.
At one point I asked the point of all this discussion. Does it have practical implications? I was told, "not really, but the formalism is crucial for the integrity and advancement of the field."
As I've learnt a bit more on the topic, I've understood that such sets (e.g., non-measurable with respect to the Lebesgue measure) are contingent on the acceptance of the axiom of choice. I further understand this axiom as being able to prove existence without construction—e.g., for the Vitali set, partitioning $[0, 1]$ into infinitely many "bins" and choosing a representative from each bin. An honest-to-goodness proof about this set would need to describe how such a representative is chosen; since there are infinitely many bins, in order to have a finite proof, instead of manually choosing representatives, you must construct a rule to accept one of the bins and output a representative. The axiom of choice just says that such a rule exists; accepting it essentially allows you to complete the proof of existence while skipping the step of actually constructing a rule for a given set of sets. Lastly, I understand that no one has yet constructed such a rule (though I don't know if this would be impossible—the answer in this MO post says it "seems unprovable").
The short of this is that, not only will a non-measurable set ever come up in practice, no one can even point you to one in concept. No hard examples can be given—proofs about them just say they exist provided your acceptance of choice. They're just out there in the aether, existing only if you believe them to exist but completely invisible both in practice and in concept. It's like the invisible teapot in space—you can believe it's there but it has basically no implications whatsoever.
Provided this understanding is correct, my question is: why do we care to handle the "edge cases" of non-measurable sets, especially in engineering classes like the ones I was in, but even in pure math? In what sense is it "crucial to the integrity and advancement of the field"?
Further, and in a similar vein, why do we bother with the axiom of choice? I understand it is independent of ZF but has great utility, i.e., extending finite proofs to infinite cases; but it seems like a shortcut to a real proof that comes at the cost of creating mathematical spectres that you now have to worry about.
Note: in contrast to this question, my question includes two questions. 1) What real benefit does the axiom of choice bring? (From my view, just assuming something exists but not having any examples is not beneficial. The difference from @Lee Mosher's comment, "From this point of view the axiom of choice is just another existence axiom", is that, for instance, assuming infinite sets exists is immediately appealing because we previously had ideas of such sets. It's not intuitive in the same way that we can always necessarily make infinity choices at once.) 2) Supposing choice, non-measurable sets seem like absolute trivia. Perhaps the most arcane textbooks should mention them, but what benefit does it bring to discuss sets you cannot give an example of and which you only assume exist? The mentioned question only asks, "Why do we accept the existence of non-measurable sets?" which is close, but not identical, to 1) and does not touch on 2).
Further, the answer in said question says that non-measurable sets are not exclusive to systems with choice, while the answer in this question says otherwise.
I hope it is therefore obvious that this question is not remotely a duplicate of the first linked question. Asking "Why would you accept non-measurable sets?" and being told, "because choice implies them" (the contents of the supposed duplicate), is a completely separate question from "Supposing you accept choice, what is the merit or exigency for discussing non-measurable sets? In what way is it necessary to handle them for the advancement of the field (say, statistical ML)? Where is the real benefit coming in?"