Separation problems in binary models can mislead even large datasets if both dependent and independent variables are rare, such as interstate relations data. Researchers often use Firth's penalized likelihood approach to overcome this issue, but our simulations show it may lead to incorrect inferences. An alternative solution involves Bayesian methods with weakly-informative priors centered at zero—specifically the Cauchy prior—to avoid these pitfalls.
