205
the measurement would directly lead to a result, and the measurement uncertainty would not be affected by this.
• S2 might rely on the model of the measurand, but only for evaluating the contribution of the difference of temperatures to definitional uncertainty, what traditionally is called a “bias”, i.e., the estimate of a systematic error (JCGM, 2012:
2.18) induced by such a difference; were the model discovered to be wrong, the
only consequence would be that definitional uncertainty might be under- or overevaluated. Under the assumption that definitional uncertainty is compared with
measurement uncertainty, not propagated as a component of the uncertainty budget (see Sect. 3.2.4), also when using on this strategy measurement results do not
depend on the validity of the model of the measurand.
• S3 entirely relies on the model of the measurand: measurement results are
obtained by computing a combination function that models the relationship
among properties of the object: were the relation between temperature and volume discovered to be significantly different from the one used to compute the
correction, measurement results should be changed accordingly; moreover, the
combination function is exploited for propagating the uncertainties of (actual)
volume and temperature, to obtain the uncertainty of (modeled) volume. When
using this strategy, measurement results do depend on the validity of the model of
the measurand.
Both S1 and S2 fulfill the conditions in the left column of Table 7.1 and therefore
can be considered uncontroversial cases of direct measurement, thus showing that a
measurement may be direct even if the presence of some affecting properties is
acknowledged in the model of the measurand. S3 may be described as the two
(direct) measurements of (effective) volume and temperature, followed by a computational correction for obtaining a value of (intended) volume by computing a combination function which is (part of) the model of the measurand. Hence this structure
fits with the conditions in the right column of Table 7.1. However, S3 is not a typical
case of indirect measurement, such as when the density of a body is measured by
computing its value as a function of the values of the mass and the volume of the
body. In the next section, we further analyze this difference.
7.2.3 Refining the distinction between direct and indirect
measurement: second step
At the core of a model of a measurand is the hypothesis that the general property of
which the measurand is an instance is an element of a network of properties with
which the general property is in a lawlike relation (Sect. 6.6 presents a short analysis
of this important subject). There are two basic reasons for exploiting such a network
in calculating a value of the measurand, as exemplified by the two cases mentioned above:
7.2 Direct and indirect measurement
the measurement would directly lead to a result, and the measurement uncertainty would not be affected by this.
• S2 might rely on the model of the measurand, but only for evaluating the contribution of the difference of temperatures to definitional uncertainty, what traditionally is called a “bias”, i.e., the estimate of a systematic error (JCGM, 2012:
2.18) induced by such a difference; were the model discovered to be wrong, the
only consequence would be that definitional uncertainty might be under- or overevaluated. Under the assumption that definitional uncertainty is compared with
measurement uncertainty, not propagated as a component of the uncertainty budget (see Sect. 3.2.4), also when using on this strategy measurement results do not
depend on the validity of the model of the measurand.
• S3 entirely relies on the model of the measurand: measurement results are
obtained by computing a combination function that models the relationship
among properties of the object: were the relation between temperature and volume discovered to be significantly different from the one used to compute the
correction, measurement results should be changed accordingly; moreover, the
combination function is exploited for propagating the uncertainties of (actual)
volume and temperature, to obtain the uncertainty of (modeled) volume. When
using this strategy, measurement results do depend on the validity of the model of
the measurand.
Both S1 and S2 fulfill the conditions in the left column of Table 7.1 and therefore
can be considered uncontroversial cases of direct measurement, thus showing that a
measurement may be direct even if the presence of some affecting properties is
acknowledged in the model of the measurand. S3 may be described as the two
(direct) measurements of (effective) volume and temperature, followed by a computational correction for obtaining a value of (intended) volume by computing a combination function which is (part of) the model of the measurand. Hence this structure
fits with the conditions in the right column of Table 7.1. However, S3 is not a typical
case of indirect measurement, such as when the density of a body is measured by
computing its value as a function of the values of the mass and the volume of the
body. In the next section, we further analyze this difference.
7.2.3 Refining the distinction between direct and indirect
measurement: second step
At the core of a model of a measurand is the hypothesis that the general property of
which the measurand is an instance is an element of a network of properties with
which the general property is in a lawlike relation (Sect. 6.6 presents a short analysis
of this important subject). There are two basic reasons for exploiting such a network
in calculating a value of the measurand, as exemplified by the two cases mentioned above:
7.2 Direct and indirect measurement