  We provide methods to validate and compare sensor outputs, or inference
algorithms applied to sensor data, by adapting statistical scoring rules. The
reported output should either be in the form of a prediction interval or of a
parameter estimate with corresponding uncertainty. Using knowledge of the
`true' parameter values, scoring rules provide a method of ranking different
sensors or algorithms for accuracy and precision. As an example, we apply the
scoring rules to the inferred masses of cattle from ground force data and draw
conclusions on which rules are most meaningful and in which way.
