  We expand the scope of the statistical notion of error probability, i.e., how
often large deviations are observed in an experiment, in order to make it
directly applicable to quantum tomography. We verify that the error probability
can decrease at most exponentially in the number of trials, derive the explicit
rate that bounds this decrease, and show that a maximum likelihood estimator
achieves this bound. We also show that the statistical notion of
identifiability coincides with the tomographic notion of informational
completeness. Our result implies that two quantum tomographic apparatuses that
have the same risk function, (e.g. variance), can have different error
probability, and we give an example in one qubit state tomography. Thus by
combining these two approaches we can evaluate, in a reconstruction independent
way, the performance of such experiments more discerningly.
