  Statistical methods of presenting experimental results in constraining the
neutrino mass hierarchy (MH) are discussed. Two problems are considered and are
related to each other: how to report the findings for observed experimental
data, and how to evaluate the ability of a future experiment to determine the
neutrino mass hierarchy, namely, sensitivity of the experiment. For the first
problem where experimental data have already been observed, the classical
statistical analysis involves constructing confidence intervals for the
parameter {\Delta}m^2_{32}. These intervals are deduced from the parent
distribution of the estimation of {\Delta}m^2_{32} based on experimental data.
Due to existing experimental constraints on |{\Delta}m^2_{32}|, the estimation
of {\Delta}m^2_{32} is better approximated by a Bernoulli distribution (a
Binomial distribution with 1 trial) rather than a Gaussian distribution.
Therefore, the Feldman-Cousins approach needs to be used instead of the
Gaussian approximation in constructing confidence intervals. Furthermore, as a
result of the definition of confidence intervals, even if it is correctly
constructed, its confidence level does not directly reflect how much one
hypothesis of the MH is supported by the data rather than the other hypothesis.
We thus describe a Bayesian approach that quantifies the evidence provided by
the observed experimental data through the (posterior) probability that either
one hypothesis of MH is true. This Bayesian presentation of observed
experimental results is then used to develop several metrics to assess the
sensitivity of future experiments. Illustrations are made using a simple
example with a confined parameter space, which approximates the MH
determination problem with experimental constraints on the |{\Delta}m^2_{32}|.
