Binary data is crucial in epidemiology because it allows researchers to easily analyze the relationship between exposures and outcomes. For example, when studying the risk factors for a disease, researchers can examine whether exposure to a certain factor (e.g., smoking) increases the likelihood of a binary outcome (e.g., lung cancer).