> I watched some of the youtube, and in the thought experiment she proposes she take a continuous trait(height) and arbitrarily splits it into two buckets. And talks about how this is kinda silly.

Something can be continuous but still very clustered, and the extent to which it's "silly" depends on the uniformity of the distribution. It can still be useful to label the clusters in the distribution.

Another reason to subdivide a continuous dimension is that there could be a threshold after which some other dependent variable begins its inflection point (think of a hockeystick distribution). For example, there's probably some blood pressure value beyond which we begin to rapidly see serious adverse health effects--it's useful to call this "hypertension" or something. For another example, there's a threshold for the average temperature of the planet beyond which global warming "runs away". These are useful thresholds even though the dimensions are continuous.