  Consistent experiment data are crucial to adjust parameters of physics models
and to determine best estimates of observables. However, often experiment data
are not consistent due to unrecognized systematic errors. Standard methods of
statistics such as $\chi^2$-fitting cannot deal with this case. Their
predictions become doubtful and associated uncertainties too small. A human has
then to figure out the problem, apply corrections to the data, and repeat the
fitting procedure. This takes time and potentially costs money. Therefore, a
Bayesian method is introduced to fit and analyze inconsistent experiment data.
It automatically detects and resolves inconsistencies. Furthermore, it allows
to extract consistent subsets from the data. Finally, it provides an overall
prediction with associated uncertainties and correlations less prone to the
common problem of too small uncertainties. The method is foreseen to function
with a large corpus of data and hence may be used in nuclear databases to deal
with inconsistencies in an automated fashion.

Nuclear Theory;Nuclear Experiment;Data Analysis, Statistics and Probability