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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.
Table of Contents
Foundational ideas in measurement
Components of error or uncertainty
Foundational ideas in probability and statistics
The randomization of systematic errors
Beyond the standard confidence interval
Evaluation of Uncertainty
Evaluation using the linear approximation
Evaluation without the linear approximations
Uncertainty information fit for purpose
Measurement of vectors and functions
Why take part in a measurement comparison?
An assessment of objective Bayesian methods
A guide to the expression of uncertainty in measurement
Measurement near a limit - an insoluble problem?
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