  This paper looks at effects, due to the boundary, on inference in logistic
regression. It shows that first -- and, indeed, higher -- order asymptotic
results are not uniform across the model. Near the boundary, effects such as
high skewness, discreteness and collinearity dominate, any of which could
render inference based on asymptotic normality suspect. A highly interpretable
diagnostic tool is proposed allowing the analyst to check if the boundary is
going to have an appreciable effect on standard inferential techniques.
