A discrete probability distribution describes the likelihood of different outcomes in a scenario where the possible results are distinct and countable. Each outcome has a specific probability, and the sum of all probabilities in the distribution equals 1. Common examples include the outcomes of rolling a die or flipping a coin. These distributions can be represented using a probability mass function (PMF), which assigns probabilities to each possible outcome. Discrete distributions are useful in various fields, including statistics, finance, and gambling, as they help model situations where events occur in separate, identifiable categories.