> You know the distribution of the phenomenon under study If you know the distribution of the phenomenon under study you dont need ML, that is what probability

> You know the distribution of the phenomenon under study If you know the distribution of the phenomenon under study you dont need ML, that is what probability is for. > or make an explicit assumption and assume the risk of being wrong No.You have the Bias/Variance tradeoff here.You can make an explicit assumption about your model or not. > Using (1), you calculate how much data you need so you get an estimation error below x% This is extremely complicated for anything except the most trivial toy examples, probably not solvable at all and definitely not the way biological intelligent systems (aka some humans) do it.