distribution

A _shape of uncertainty_: how probability is allocated across possible outcomes. A discrete distribution assigns a number to each outcome — `P(X = "cat") = 0.6, P(X = "dog") = 0.3, P(X = "bird") = 0.1`. A continuous distribution assigns _density_ across an interval — there is no probability at any single point, only over a range. The numbers must sum (discrete) or integrate (continuous) to 1, because _something_ must happen. A single probability is one number; a distribution is the whole shape behind it. Most quantities a model predicts, an asset can return, or a pixel can take, are not single numbers but distributions — and the _spread_ of those distributions is often what matters more than the center.