Survival analysis is the study of the distribution of time-to-event data, that is, the times from an initiating event to some terminal event. A distinguishing feature of survival data is the occurrence of right censoring, where the terminal event for some individuals is not observed; instead, it is only known that this event is at least later than a given point in time. This article presents a brief historical review and focuses on central statistical approaches: non- and semiparametric estimation (Kaplan-Meier, Cox regression) and hypothesis testing (log-rank). We also add brief comments on parametric models and list some important reference works.