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.