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Bayesian modeling techniques enable sensitivity analyses that incorporatedetailed expectations regarding future experiments. A model-based approach alsoallows one to evaluate inferences and predicted outcomes, by calibrating (ormeasuring) the consequences incurred when certain results are reported. Wepresent procedures for calibrating predictions of an experiment's sensitivityto both continuous and discrete parameters. Using these procedures and a newBayesian model of the $\beta$-decay spectrum, we assess a high-precision$\beta$-decay experiment's sensitivity to the neutrino mass scale and ordering,for one assumed design scenario. We find that such an experiment could measurethe electron-weighted neutrino mass within $\sim40\,$meV after 1 year (90$\%$credibility). Neutrino masses $>500\,$meV could be measured within$\approx5\,$meV. Using only $\beta$-decay and external reactor neutrino data,we find that next-generation $\beta$-decay experiments could potentiallyconstrain the mass ordering using a two-neutrino spectral model analysis. Bycalibrating mass ordering results, we identify reporting criteria that can betuned to suppress false ordering claims. In some cases, a two-neutrino analysiscan reveal that the mass ordering is inverted, an unobtainable result for thetraditional one-neutrino analysis approach.

Bayesian Analysis of a Future Beta Decay Experiment's Sensitivity to Neutrino Mass Scale and Ordering