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).

Moiré patterns are the same thing, but if you put your video signal through an analog low-pass filter before sampling, then you wouldn't get Moiré patterns. Audio is typically low-passed before sampling for this reason.

"Presumably though the effects of aliasing will also also produce distortions at a smaller scale, which might not be as obvious"

Why? What do you mean? What scale? What do you mean by distortion? The only only only thing sampling does is move frequencies beyond the nyquest frequency into the sampled signal by taking the signal at f, and sending it to f mod f_n where f_n is the nyquist frequency.

Aliasing on a triangle wave can sound quite beautiful:

http://kmkeen.com/awk-music/