A joint distribution describes the probability of two or more random variables occurring simultaneously. It provides a comprehensive view of how these variables interact with each other, showing the likelihood of different combinations of outcomes. For example, in a study involving height and weight, the joint distribution can reveal how likely it is for individuals to have specific height and weight pairs. In mathematical terms, the joint distribution can be represented using a joint probability mass function for discrete variables or a joint probability density function for continuous variables. This concept is essential in fields like statistics, machine learning, and economics, where understanding the relationships between multiple variables is crucial for analysis and decision-making.