The calibration and uncertainty evaluation by elementary measurement models

Nature has often made necessary things simple (elementary) and complicated ones unnecessary. This can be applied to measurement models of the large amount of working measurement standards and working measuring instruments. Nevertheless, if measurement model is elementary, it does not mean that it is primitive. It should be formulated according to the sufficiency, mathematical completeness and correctness principles. The attempt to formulate models according to the mentioned principles is introduced. Models are called elementary, as measurement result is function of one or two homogeneous measured quantities. Thus, measurement result is a single reading of the measurement standard or measuring instrument or average value of several readings, or bias as the disparity between readings of the calibrated measuring instrument and measurement standard. Notwithstanding the elementary measurement models simplicity, many variants of solutions are obtained in the process of the measurement result uncertainty evaluation by these models. Publication demonstrates how to choose the best uncertainty evaluation from many variants of solutions depending on whether single readings or average of several readings is included to measurement model. The best choice of the measurement model depends on resolution of the indicating measuring instrument. Moreover, the best choice depends on the measurement standard used for calibration, which is material measure or measuring instrument and depends on the calibration object: material measure or measuring instrument.