 Let us begin with the mathematics. Probability theory assumes that we have a set of “possibilities” or “outcomes”, called a sample space, regarded as ways the world could be, or the possible outcomes of a random experiment. For many purposes, an infinite set is assumed. For example, we may toss a coin repeatedly and be interested in how many tosses it takes until we see the first heads. The number could be 1, or 2, or 3, or … Here, the sample space is denumerable. Or we might consider picking a point at random from the [0, 1] interval of the real line—e.g., we might imagine throwing an idealized dart at a representation of that interval, and consider the point on which it lands. Here, the sample space is uncountable, because it is infinitely divisible and has limits of sequences, but bounded. Or we might consider sampling a quantity that is governed by a normal distribution, the bell-shaped distribution that is used to model various quantities in the real world. Here, infinitude enters twice over: the sample space is both uncountable and unbounded, being the entire real line.