No excuses necessary I think that's a great question. Yes, like in the article - you can remove extremely unlikely to be real things, unlikely to be real could be because of things such as being extremely conditional (pentavalent carbon for example) or simply implausible. The point is that by preventing the model from learning a distribution that captures the extremely rare though real instances of things, you end up with a distribution that departs from whatever the reality of the situation is. You end up with a biased model, and that is highly unlikely to be a good thing.

Bottom line, it is better to let a model learn the full distribution then clip predictions that come from extreme ends of the distribution, rather than preventing the model from considering these extremes in the first place. Either way you don't get predictions from the extremes, but the model allowed to consider extremes is more likely to do interesting things.