  I propose a finite sample inference procedure that uses a likelihood function
derived from the randomization process within an experiment to conduct
inference on various quantities that capture heterogeneous intervention
effects. One such quantity is the number of defiers---individuals whose
treatment runs counter to the intervention. Results from the literature make
informative inference on this quantity seem impossible, but they rely on
different assumptions and data. I only require data on the cross-tabulations of
a binary intervention and a binary treatment. Replacing the treatment variable
with a more general outcome variable, I can perform inference on important
quantities analogous to the number of defiers. I apply the procedure to test
safety and efficacy in hypothetical drug trials for which the point estimate of
the average intervention effect implies that at least 40 out of 100 individuals
would be saved. In one trial, I infer with 95% confidence that at least 3
individuals would be killed, which could stop the drug from being approved.
