The paper addresses the challenges in analyzing rare events data, where the number of events (e.g., wars, vetoes, political activism) is significantly fewer than the number of nonevents. The authors highlight two main issues: (1) popular statistical methods, such as logistic regression, can underestimate the probability of rare events, and (2) common data collection strategies are inefficient for rare events data. They propose corrections to these issues, including efficient sampling designs that collect all available events and a small fraction of nonevents, which can save up to 99% of data collection costs. The paper also provides methods to integrate these corrections and software to implement them. The authors demonstrate the effectiveness of their methods through Monte Carlo experiments and empirical examples.The paper addresses the challenges in analyzing rare events data, where the number of events (e.g., wars, vetoes, political activism) is significantly fewer than the number of nonevents. The authors highlight two main issues: (1) popular statistical methods, such as logistic regression, can underestimate the probability of rare events, and (2) common data collection strategies are inefficient for rare events data. They propose corrections to these issues, including efficient sampling designs that collect all available events and a small fraction of nonevents, which can save up to 99% of data collection costs. The paper also provides methods to integrate these corrections and software to implement them. The authors demonstrate the effectiveness of their methods through Monte Carlo experiments and empirical examples.