Measurements of any kind, in any experiment, are always subject to uncertainties or errors, as they are more often called. We will argue in this section that the measurement process is, in fact, a random process described by an abstract probability distribution whose parameters contain the information desired. The results of a measurement are then samples from this distribution which allow an estimate of the theoretical parameters. In this view, measurement errors can be seen then as sampling errors.
Before going into this argument, however, it is first necessary to distinguish between two types of errors: systematic and random.