Abstract: Logistic regression is a widely used statistical model. In practice, many researchers tend to ignore the requirements of the logistic regression on sample size or take over the sample size due to “enough subjects were included in the study populaton”, which fails to explore the relationship between the primary outcome and main impact indicator and ignores two types of errors. This paper introduced three Logistic regression sample size calculation methods, supplemented by examples to help researchers reasonably complete the design and implementation of the study.