  The technical breakthroughs of multiple detectors developed by Daya Bay and
RENO collaborations have gotten great attention. Yet the optimal determination
of neutrino mixing parameters from reactor data depends on the statistical
method and demands equal attention. We find that a straightforward method using
a minimal parameters will generally outperform a multi-parameter method by
delivering more reliable values with sharper resolution. We review standard
confidence levels and statistical penalties for models using extra parameters,
and apply those rules to our analysis. We find that the methods used in recent
work of the Daya Bay and RENO collaborations have several undesirable
properties. The existing work also uses non-standard measures of significance
which we are unable to explain. A central element of the current methods
consists of variationally fitting many more parameters than data points. As a
result the experimental resolution of $\sin ^{2}(2\theta _{13})$ is degraded.
The results also become extremely sensitive to certain model parameters that
can be adjusted arbitrarily. The number of parameters to include in evaluating
significance is an important issue that has generally been overlooked. The
measures of significance applied previously would be consistent if and only if
all parameters but one were considered to have no physical relevance for the
experiment's hypothesis test. Simpler, more transparent methods can improve the
determination of the mixing angle $\theta _{13}$ from reactor data, and exploit
the advantages from superb hardware technique of the experiments. We anticipate
that future experimental analysis will fully exploit those advantages.
