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Conclusion

  • Semyon G. Rabinovich
Chapter

Abstract

Historically, metrology emerged as a science of measures. Even in the middle of the last century, metrology was considered to be the science of measurements concerning the creation and maintenance of measurement standards [37]. With this approach, the theory of accuracy of measurements was limited to the problems of estimation of the accuracy of multiple measurements and only to random errors. Math statistics was a natural fit for these problems. As a result, the science of measurement data processing was in essence the reformulation of math statistics in the context of random errors.

This state of affairs can be clearly seen by examining relatively recent books on the subject, for example, Data Analysis for Scientists and Engineers by S. Meyer (1975), Data Reduction and Error Analysis for Physical Sciences by Ph. Bevington and D. Robinson (1992), and Measurement Theory for Engineers by I. Gertsbakh (2003). Even the book The Statistical Analysis of Experimental Data (National Bureau of Standards, 1964) by J. Mandel, which stands out by considering concrete measurement problems, remained within the above confines. Nevertheless, because this purely mathematical theory found practical applications, even in a restricted case of random errors in multiple measurements, this theory obtained the status of the classical theory of measurement data processing.

In the meantime, this theory did not satisfy practical needs. In particular, every practitioner knew that in addition to random errors, there are systematic errors, and the overall inaccuracy of the measurement result combined both of these components. But the classical theory ignored this fact and, furthermore, not so long ago considered it incorrect to combine these two components. There were other practical problems ignored by the classical theory. As a result, those who encountered these problems in their practice resorted to ad hoc and often incorrect methods. For example, in the case of single measurements, the measurement errors were often equated to the fiducial error of the measuring device used (see Chap. 2Measuring Instruments and Their Propertieschapter.2.26), which is wrong. To account for systematic errors in a multiple measurement, people often simply added them to the random errors, which overestimated the inaccuracy of the result.

Keywords

Random Error Classical Theory Measured Quantity Multiple Measurement Standard Uncertainty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 2.
    Guide to the Expression of Uncertainty in Measurement, ISO (1995).Google Scholar
  2. 10.
    Process Instrumentation Terminology, American National Standard ANSI/ISA S51.1 (1979).Google Scholar
  3. 12.
    Metrology, Terms and Definitions, State Standard of the Soviet Union, GOST16263–70, Moscow (1970).Google Scholar
  4. 13.
    Evaluation of measurement data – Supplement 1 to the “Guide to the expression of uncertainty in measurement”. Propagation of distributions using a Monte Carlo method. Final draft. Joint Committee for Guides in Metrology (JCGM). September 2006.Google Scholar
  5. 16.
    W. Bich, M.G. Cox, P.M. Harris. “Evolution of the “Guide to the expression of uncertainty in measurement”,” Metrologia 43, 161–166 (2006).CrossRefADSGoogle Scholar
  6. 27.
    V.A. Granovskii, Dynamic Measurements, Principles of Metrological Support (Energoatomizdat, Leningrad, 1984).Google Scholar
  7. 31.
    R. Kacker, A. Jones, “On use of Bayesian statistics to make the “Guide to the expression of uncertainty in measurement” consistent,” Metrologia 40, 235–248 (2003).CrossRefADSGoogle Scholar
  8. 37.
    M.F. Malikov, Foundations of Metrology (Committee on Measures and Measuring Devices at the Council of Ministers of the USSR, Moscow, 1949).Google Scholar
  9. 42.
    S. Rabinovich, “Towards a new edition of the “Guide to the expression of uncertainty in measurement”,” Accreditation and Quality Assurance 12(11) 603–608, (2007).CrossRefGoogle Scholar
  10. 44.
    S. Rabinovich, Measurement Errors and Uncertainties: Theory and Practice, 3rd ed (Springer, New York, 2005).MATHGoogle Scholar
  11. 46.
    S.G. Rabinovich, Measurement Errors (Energia, Leningrad, 1978).Google Scholar
  12. 51.
    K.P. Shirokov, V.O. Arutyunov, E.M. Aristov, V.A. Granovskii, W.S. Pellinez, S.G. Rabinovich, D.F. Tartakovskii, “Basic concepts of the theory of dynamic measurements,” Proceedings of the 8th IMEKO Congress (Akademiai Kiado, Budapest, 1980).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Basking RidgeUSA

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