52
The important point here is the acknowledgment that “every measurement is
tainted by imperfectly known errors, so that the significance which one can give to
the measurement must take account of this uncertainty” (ISO, 1993: Foreword).
While errors generate uncertainty in measurement, nothing in principle precludes
the possibility that uncertainty has other causes as well: this suggests that measurement uncertainty is an encompassing concept, and justifies the current trend of moving away from the error approach and towards the uncertainty approach witnessed
in both the VIM and the GUM.
3.2.3 The Uncertainty Approach
Like the error approach, the uncertainty approach can be characterized primarily as
a framework that provides functional solutions to implement what we have called an
informational strategy to cope with the observed variability of measurement results,
and secondarily as a conceptualization that can be included as a background justification for the way in which uncertainty is understood and discussed.
9
The starting point is that, even when the measurement is not repeated, the information available in the context of the measurement may allow the measurer to
acknowledge that the obtained results have a limited quality, due in particular to the
quality of the measuring instrument and of the available information on the instrument calibration and on the influence properties. Compared to the error approach,
the focus here is less on the experimental errors themselves and more on the state of
partial knowledge of the measurer, who designs and performs the measurement for
the explicit purpose of gaining information on the measurand, with the acknowledgment that “complete” information (whatever this may actually mean) is unobtainable even by the best possible measurement.
10
As related to measurement results,
the concept emphasizes this incompleteness, and the
standardization of the methods for identifying sources of uncertainty and formalizing the quantitative evaluation of their contributions and their combination provides
an even more solid common ground for measurement (JCGM, 2008a, 2008b: 0.3):
9 The uncertainty that is being addressed in this section (and elsewhere in this and other chapters)
is not associated with sampling variability that is with the uncertainty that is due to a situation
where a statistical result is based on a sample from a population of properties of distinct objects,
where a parameter of the statistical distribution is being estimated—this is usually denoted as
sampling error.
10 This may be considered a measurement-specific case of the fundamental distinction between
models and modeled entities, sometimes presented in terms of maps versus territory: the only
“perfect” map is the territory itself, so that, paradoxically, aiming at a perfect map makes the mapping process useless (the subject of On Exactitude in Science, a delightful short story by Jorge Luis
Borges). Analogously, a claimed-to-be-perfect measurement would directly exhibit the property
under measurement (the perfect representative of itself, indeed), thus making the process of measuring pointless.
3 Technical and cultural contexts for measurement systems
The important point here is the acknowledgment that “every measurement is
tainted by imperfectly known errors, so that the significance which one can give to
the measurement must take account of this uncertainty” (ISO, 1993: Foreword).
While errors generate uncertainty in measurement, nothing in principle precludes
the possibility that uncertainty has other causes as well: this suggests that measurement uncertainty is an encompassing concept, and justifies the current trend of moving away from the error approach and towards the uncertainty approach witnessed
in both the VIM and the GUM.
3.2.3 The Uncertainty Approach
Like the error approach, the uncertainty approach can be characterized primarily as
a framework that provides functional solutions to implement what we have called an
informational strategy to cope with the observed variability of measurement results,
and secondarily as a conceptualization that can be included as a background justification for the way in which uncertainty is understood and discussed.
9
The starting point is that, even when the measurement is not repeated, the information available in the context of the measurement may allow the measurer to
acknowledge that the obtained results have a limited quality, due in particular to the
quality of the measuring instrument and of the available information on the instrument calibration and on the influence properties. Compared to the error approach,
the focus here is less on the experimental errors themselves and more on the state of
partial knowledge of the measurer, who designs and performs the measurement for
the explicit purpose of gaining information on the measurand, with the acknowledgment that “complete” information (whatever this may actually mean) is unobtainable even by the best possible measurement.
10
As related to measurement results,
the concept
standardization of the methods for identifying sources of uncertainty and formalizing the quantitative evaluation of their contributions and their combination provides
an even more solid common ground for measurement (JCGM, 2008a, 2008b: 0.3):
9 The uncertainty that is being addressed in this section (and elsewhere in this and other chapters)
is not associated with sampling variability that is with the uncertainty that is due to a situation
where a statistical result is based on a sample from a population of properties of distinct objects,
where a parameter of the statistical distribution is being estimated—this is usually denoted as
sampling error.
10 This may be considered a measurement-specific case of the fundamental distinction between
models and modeled entities, sometimes presented in terms of maps versus territory: the only
“perfect” map is the territory itself, so that, paradoxically, aiming at a perfect map makes the mapping process useless (the subject of On Exactitude in Science, a delightful short story by Jorge Luis
Borges). Analogously, a claimed-to-be-perfect measurement would directly exhibit the property
under measurement (the perfect representative of itself, indeed), thus making the process of measuring pointless.
3 Technical and cultural contexts for measurement systems