There is a difference between 'can' and "is the central question". As I said in my comment [0], traditionally statistics is about making claims about the nature

There is a difference between 'can' and "is the central question". As I said in my comment [0], traditionally statistics is about making claims about the nature of the population from a sample. If one is operating with a parametric model, the running assumption is that one knows absolutely everything about the population except for a few parameters. Those are estimated from the data. One you have done that, one can indeed use those to do prediction and if you were lucky that the data did come from the distribution assumed, then you wont do too badly, otherwise you could perform very poorly. ML on the other hand short circuits the step of making any claims about, or even trying to understand the population. ML straight away jumps to claims about prediction quality. Note there is non-parametric statistics, but even there (barring edge cases) the central object of inquiry is the population. There is also prequential statistics that also jumps directly to claims about prediction without making any claims about understanding the population. Prequential stats is fascinating, and it is what is closes to ML in spirit. [0] https://news.ycombinator.com/item?id=18591143 https://news.ycombinator.com/item?id=18591143