A large sample approximation to the variance of the Kaplan–Meier estimator, an exponential survival distribution, and a uniform entry over [0, accural time] are assumed. For theoretical background, see Fleming & Harrington (1991) and Andersen, Borgan, Gill & Keiding (1993). Moreover, various transformations for the Kaplan–Meier estimator are supported in this application. Log-minus-log, logit, and arcsine square-root transformed confidence intervals have better performance than linear and log transformed confidence intervals (Bie et al., 1987; Borgan & Liestøl, 1990). The required sample size and the performance depend on the method of the transformation. The well known SWOG's calculator (One Sample Nonparametric Survival) use the log transformation, but a sample size formula different form this application is used. We submitted a research paper to a journal about this results with numerical evaluations via simulations. As a result, empirical power of the sample size formula with the arcsine square-root transformation is close to the nominal power than the other transformations. Therefore, this application uses the arcsine square-root transformation as default. When performing analysis, it is reccomended to use the arcsine square-root transformation or more conservative (i.e., log-minus-log or logit) transformation.