Discrete distributions are statistical distributions that describe variables taking on distinct, separate values, often representing counts or whole-number outcomes. These distributions are particularly useful when dealing with data that consists of integer or count-based values, which cannot be split into fractions. Examples of discrete data include the number of students in a class or the number of cars passing through an intersection.

There are several types of discrete distributions, with two of the most common being the Binomial and Poisson distributions. The Binomial distribution records how often a success appears in a set number of independent trials, each with a fixed chance of success. For instance, it can be used to calculate the probability of getting a certain number of heads when flipping a coin multiple times. The Poisson distribution, on the other hand, estimates how many events occur within a fixed interval of time or space, such as the number of emails received in an hour.

Understanding discrete distributions is essential for analyzing data and making informed decisions based on statistical evidence ³⁵.