  When the data do not conform to the hypothesis of a known sampling-variance,
the fitting of a constant to a set of measured values is a long debated
problem. Given the data, fitting would require to find what measurand value is
the most trustworthy. Bayesian inference is here reviewed, to assign
probabilities to the possible measurand values. Different hypothesis about the
data variance are tested by Bayesian model comparison. Eventually, model
selection is exemplified in deriving an estimate of the Planck constant.
