  Measuring observables to constrain models using maximum-likelihood estimation
is fundamental to many physics experiments. The Profiled Feldman-Cousins method
described here is a potential solution to common challenges faced in
constructing accurate confidence intervals: small datasets, bounded parameters,
and the need to properly handle nuisance parameters. This method achieves more
accurate frequentist coverage than other methods in use, and is generally
applicable to the problem of parameter estimation in neutrino oscillations and
similar measurements. We describe an implementation of this method in the
context of the NOvA experiment.
