Presumably though the effects of aliasing will also also produce distortions at a smaller scale, which might not be as obvious. That would result in a loss of i

Presumably though the effects of aliasing will also also produce distortions at a smaller scale, which might not be as obvious. That would result in a loss of information, but would not be quite so easy to spot and filter out. The pathological case is really just a good illustration of what happens and why, whereas what's trickier is understanding the true impact it has on real-world data. For instance, you won't notice Moiré patterns in photos very often, but it's easy to construct a test image that demonstrates the problem. The underlying issue of sampling error hasn't gone away in the photo: it just gets lost in the background noise or among smooth-shaded surfaces without hard edges, which makes it harder to see. But maybe now and then you'll have a sharp edge in a real-world image where the effect is noticeable. Depending on how close it is to the pathological case, it might not jump out and make itself immediately obvious. I mention Moiré patterns because these are (I believe) precisely the same effect as the given audio aliasing example. Moiré patterns aren't a form of sampling error per se, but they can be caused by uniform sampling (which is how digital quantisation typically works for both audio and images).