What describes how probabilities are distributed over the values of a random variable?

The concept of a probability distribution is fundamental in statistics and probability theory. It describes how probabilities are allocated across the possible values of a random variable, essentially detailing the likelihood of different outcomes. A probability distribution can take various forms depending on whether the random variable is discrete or continuous.

For example, in the case of a discrete random variable, the probability distribution can be demonstrated through a probability mass function, which gives the probabilities of each individual outcome. Conversely, for a continuous random variable, the distribution can be represented by a probability density function, which describes probabilities over intervals rather than distinct values.

The other terms related to this context serve specific roles; while the probability mass function pertains specifically to discrete variables, and probability density to continuous cases, the broader term 'probability distribution' encompasses all these possibilities. Hence, it accurately captures the overall concept of how probabilities are arranged for any type of random variable.