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Uncertainty

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Part of the book series: Springer Series in Measurement Science and Technology ((SSMST))

Abstract

Suppose that you go to your physician and that he measures your blood pressure. He will probably repeat the measurement a few times and will usually not obtain exactly the same result through such repetitions.

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Notes

  1. 1.

    Henceforth, we need the notion of probabilistic or random variable (we prefer the former term, although the latter is more common). Though we assume that the reader has a basic knowledge of probability theory, for the sake of convenience, we present a brief review of the probability notions used in this book in Sect. 4.1. Note in particular the notation, since we often use a shorthand one. We do not use any special conventions (such as capital or bold characters) for probabilistic variables. So the same symbol may be used to denote a probabilistic variable or its specific value. For example the probability density function of \(v\) can be denoted either as \(p_{v}(\cdot )\) or, in a shorthand notation, as \(p(v)\). For notational conventions, see also the Appendix at the end of the book, in particular under the heading “Generic probability and statistics”.

  2. 2.

    A definition of probability distribution, also (more commonly) called the probability density function for continuous variables, is provided in Sect. 4.1.8.

  3. 3.

    In general the “hat” symbol is used to denote an estimator or an estimated value. If applied to the measurand, it denotes the measurement value.

  4. 4.

    We will discuss loudness measurement in some detail in Chap. 8. Readers who are unfamiliar with acoustic quantities may consult the initial section of that chapter for some basic ideas.

  5. 5.

    In the practical implementation of the experiment, there are different ways of varying the stimulus, either through series of ascending or descending values, or as a random sequence. The variation can be controlled by the person leading the experiment or by the test subject [9, 10] . In any case, such technicalities do not lie within the sphere of this discussion.

  6. 6.

    For the notion of conditional probability, see Sects. 4.1.14.1.3 of Chap. 4, in this book, as well as any good textbook on probability theory [11].

  7. 7.

    In fact the variance of the sum (or of the difference) of two independent probabilistic variables equals the sum of their individual variances. Thus, in our case, \(\sigma _{u}^{2}=\sigma _{x_{b}}^{2}+\sigma _{x_{a}}^{2}=2\sigma ^{2}\).

  8. 8.

    The device of using the abscissae of the standard normal distribution, usually called z-points, is widely used in probability and statistics and, consequently, in psychophysics too.

  9. 9.

    Interestingly enough, Link notes that Fechner proposed a similar (but not identical) approach, which is very close the signal-detection method of the 1950s. Applying this approach, one would obtain \({\hat{\psi }}_{1}-{\hat{\psi }}_{0}=2z_{10}\sigma \), instead of the result in (2.9) [13].

  10. 10.

    The resolution of a measurement scale is the minimum variation that can be expressed with that scale (see also the glossary, in the Appendix, at the end of the book).

  11. 11.

    This formulation is somewhat qualitative but sufficient for the purpose of this informal discussion.

  12. 12.

    The BIPM and the CIPM are two of the main bodies in the international system of metrology and were established when the Metre Convention was signed (1875). A concise introduction to the organisation of the system is made in Sect. 3.7.4. and additional details on how it works are given in Sect. 10.1.

  13. 13.

    We do not use the GUM’s notation here, since we wish to be consistent with the notation used in this book. See the Appendix for further details.

  14. 14.

    In the GUM, the expected value of a quantity is regarded as a “best estimate” of that quantity.

  15. 15.

    This is usually called “loading effect” in the technical literature.

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Correspondence to Giovanni Battista Rossi .

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Rossi, G.B. (2014). Uncertainty . In: Measurement and Probability. Springer Series in Measurement Science and Technology. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8825-0_2

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