Survival analysis is a branch of statistics focused on analyzing time-to-event data, where the outcome is the time until a specific event occurs (death, failure, churn, recovery). Originating in biomedical research to model patient lifespans, survival analysis has expanded across clinical trials, reliability engineering, customer retention analytics, and social sciences. The key feature that distinguishes survival analysis from standard regression is its ability to handle censored observations—cases where the event has not yet occurred by the end of the study period. Unlike traditional methods that discard incomplete data, survival analysis incorporates partial information through specialized estimators and models, providing unbiased estimates even when exact event times are unknown for some subjects. The central insight is that knowing someone hasn't experienced the event by time $t$ is itself valuable information.