This is a reprise of an earlier post. Not only was it not up to my usual quality, it was written before we had a good term for the kind of probability distribution that's central to Probability Management and avoiding the Flaw of Averages. "Sample Distribution" captures the key characteristic and fits with the 'sample' and 'sampling' that are an important part of it. Also, Sam Savage likes it; if the guy who invented the discipline approves, I know we have a winner.
A sample distribution quantifies the uncertainty in an uncertain variable. It's always a list of numbers--in programming terms a one-dimensional array or vector. Each element of the vector is a sample value drawn without bias from the possible values of the uncertain variable. For the rest of this article, when I use 'distribution' without qualification, it refers to a sample distribution.