This tutorial paper introduces the basic concepts of survival analysis, a statistical method used to analyze time-to-event data, particularly in cancer studies. The paper emphasizes the unique challenges posed by censoring, where not all events are observed, and the skewed distribution of survival data. It discusses the importance of understanding survival and hazard functions, and how they are estimated using methods like the Kaplan-Meier estimator. The logrank test is highlighted as a widely used nonparametric method for comparing survival curves between groups. The paper also covers the interpretation of survival curves, the calculation of survival probabilities, and the estimation of hazard functions. Additionally, it addresses key requirements for survival data analysis, such as uninformative censoring, sufficient follow-up, and the need to adjust for covariates to account for patient-related factors that could affect survival. The paper concludes by emphasizing the importance of multivariate survival analysis for adjusting for confounding factors and providing a comprehensive overview of survival analysis techniques.This tutorial paper introduces the basic concepts of survival analysis, a statistical method used to analyze time-to-event data, particularly in cancer studies. The paper emphasizes the unique challenges posed by censoring, where not all events are observed, and the skewed distribution of survival data. It discusses the importance of understanding survival and hazard functions, and how they are estimated using methods like the Kaplan-Meier estimator. The logrank test is highlighted as a widely used nonparametric method for comparing survival curves between groups. The paper also covers the interpretation of survival curves, the calculation of survival probabilities, and the estimation of hazard functions. Additionally, it addresses key requirements for survival data analysis, such as uninformative censoring, sufficient follow-up, and the need to adjust for covariates to account for patient-related factors that could affect survival. The paper concludes by emphasizing the importance of multivariate survival analysis for adjusting for confounding factors and providing a comprehensive overview of survival analysis techniques.