A number of methods have been developed for unbiased and efficient approximation of small probabilities and expected values that depend heavily on tail events. Examples include importance sampling and particle splitting methods. However, successfully implementing these methods can require some care. Traditional diagnostics one might use to assess algorithm performance can be misleading, and may suggest the method is working well when in fact it is not. As a consequence, methods that combine design with rigorous performance analysis are particularly useful.