Measurement Uncertainty and Probability

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Cambridge University Press, Feb 14, 2013 - Science - 276 pages
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A measurement result is incomplete without a statement of its 'uncertainty' or 'margin of error'. But what does this statement actually tell us? By examining the practical meaning of probability, this book discusses what is meant by a '95 percent interval of measurement uncertainty', and how such an interval can be calculated. The book argues that the concept of an unknown 'target value' is essential if probability is to be used as a tool for evaluating measurement uncertainty. It uses statistical concepts, such as a conditional confidence interval, to present 'extended' classical methods for evaluating measurement uncertainty. The use of the Monte Carlo principle for the simulation of experiments is described. Useful for researchers and graduate students, the book also discusses other philosophies relating to the evaluation of measurement uncertainty. It employs clear notation and language to avoid the confusion that exists in this controversial field of science.
 

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Contents

Components of error or uncertainty
21
The randomization of systematic errors
56
Beyond the ordinary confidence interval
72
Evaluation of uncertainty
83
Evaluation using the linear approximation
98
Evaluation without the linear approximation
125
Uncertainty information fit for purpose
152
Related topics
173
Why take part in a measurement comparison?
192
Other philosophies
204
An assessment of objective Bayesian statistics
220
Guide to the Expression of Uncertainty in Measurement
237
Measurement near a limit an insoluble problem?
245
Appendix A The weak law of large numbers
259
Appendix F An alternative to a symmetric beta distribution
264
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About the author (2013)

Robin Willink is a physicist-turned-statistician, who until recently worked for Applied Mathematics, Industrial Research Ltd, the parent body of the National Metrology Institute of New Zealand. His work on statistical methods for measurement is highly regarded.

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