In metrology, measurement uncertainty is a nonnegative parameter characterizing the dispersion of the values attributed to a measured quantity. All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty. By international agreement, this uncertainty has a probabilistic basis and reflects incomplete knowledge of the quantity value.[1] The measurement uncertainty is often taken as the standard deviation of a stateofknowledge probability distribution over the possible values that could be attributed to a measured quantity. Relative uncertainty is the measurement uncertainty relative to the magnitude of a particular single choice for the value for the measured quantity, when this choice is nonzero. This particular single choice is usually called the measured value, which may be optimal in some welldefined sense (e.g., a mean, median, or mode). Thus, the relative measurement uncertainty is the measurement uncertainty divided by the absolute value of the measured value, when the measured value is not zero. Contents 1 Background
2 Indirect measurement
3 Propagation of distributions
4 Type A and Type B evaluation of uncertainty
5 Sensitivity coefficients
6
Uncertainty
6.1 Models with any number of output quantities 7 Alternative perspective 8 See also 9 References 10 Further reading 11 External links Background[edit]
The purpose of measurement is to provide information about a quantity
of interest – a measurand. For example, the measurand might be
the size of a cylindrical feature, the volume of a vessel, the
potential difference between the terminals of a battery, or the mass
concentration of lead in a flask of water.
No measurement is exact. When a quantity is measured, the outcome
depends on the measuring system, the measurement procedure, the skill
of the operator, the environment, and other effects.[2] Even if the
quantity were to be measured several times, in the same way and in the
same circumstances, a different measured value would in general be
obtained each time, assuming the measuring system has sufficient
resolution to distinguish between the values.
The dispersion of the measured values would relate to how well the
measurement is performed. Their average would provide an estimate of
the true value of the quantity that generally would be more reliable
than an individual measured value. The dispersion and the number of
measured values would provide information relating to the average
value as an estimate of the true value. However, this information
would not generally be adequate.
The measuring system may provide measured values that are not
dispersed about the true value, but about some value offset from it.
Take a domestic bathroom scale. Suppose it is not set to show zero
when there is nobody on the scale, but to show some value offset from
zero. Then, no matter how many times the person's mass were
remeasured, the effect of this offset would be inherently present in
the average of the values.
Measurement uncertainty has important economic consequences for
calibration and measurement activities. In calibration reports, the
magnitude of the uncertainty is often taken as an indication of the
quality of the laboratory, and smaller uncertainty values generally
are of higher value and of higher cost. The American Society of
Mechanical Engineers (ASME) has produced a suite of standards
addressing various aspects of measurement uncertainty.
ASME
Y displaystyle Y , about which information is required, is often related to input quantities, denoted by X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N , about which information is available, by a measurement model in the form of Y = f ( X 1 , … , X N ) , displaystyle Y=f(X_ 1 ,ldots ,X_ N ), where f displaystyle f is known as the measurement function. A general expression for a measurement model is h ( Y , displaystyle h(Y, X 1 , … , X N ) = 0. displaystyle X_ 1 ,ldots ,X_ N )=0. It is taken that a procedure exists for calculating Y displaystyle Y given X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N , and that Y displaystyle Y is uniquely defined by this equation. Propagation of distributions[edit] See also: Propagation of uncertainty The true values of the input quantities X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N are unknown. In the GUM approach, X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N are characterized by probability distributions and treated mathematically as random variables. These distributions describe the respective probabilities of their true values lying in different intervals, and are assigned based on available knowledge concerning X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N . Sometimes, some or all of X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N are interrelated and the relevant distributions, which are known as joint, apply to these quantities taken together. Consider estimates x 1 , … , x N displaystyle x_ 1 ,ldots ,x_ N , respectively, of the input quantities X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N , obtained from certificates and reports, manufacturers' specifications, the analysis of measurement data, and so on. The probability distributions characterizing X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N are chosen such that the estimates x 1 , … , x N displaystyle x_ 1 ,ldots ,x_ N , respectively, are the expectations[3] of X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N . Moreover, for the i displaystyle i th input quantity, consider a socalled standard uncertainty, given the symbol u ( x i ) displaystyle u(x_ i ) , defined as the standard deviation[3] of the input quantity X i displaystyle X_ i . This standard uncertainty is said to be associated with the (corresponding) estimate x i displaystyle x_ i . The use of available knowledge to establish a probability distribution to characterize each quantity of interest applies to the X i displaystyle X_ i and also to Y displaystyle Y . In the latter case, the characterizing probability distribution for Y displaystyle Y is determined by the measurement model together with the probability distributions for the X i displaystyle X_ i . The determination of the probability distribution for Y displaystyle Y from this information is known as the propagation of distributions.[3] The figure below depicts a measurement model Y = X 1 + X 2 displaystyle Y=X_ 1 +X_ 2 in the case where X 1 displaystyle X_ 1 and X 2 displaystyle X_ 2 are each characterized by a (different) rectangular, or uniform, probability distribution. Y displaystyle Y has a symmetric trapezoidal probability distribution in this case. Once the input quantities X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N have been characterized by appropriate probability distributions, and the measurement model has been developed, the probability distribution for the measurand Y displaystyle Y is fully specified in terms of this information. In particular, the expectation of Y displaystyle Y is used as the estimate of Y displaystyle Y , and the standard deviation of Y displaystyle Y as the standard uncertainty associated with this estimate. Often an interval containing Y displaystyle Y with a specified probability is required. Such an interval, a coverage interval, can be deduced from the probability distribution for Y displaystyle Y . The specified probability is known as the coverage probability. For a given coverage probability, there is more than one coverage interval. The probabilistically symmetric coverage interval is an interval for which the probabilities (summing to one minus the coverage probability) of a value to the left and the right of the interval are equal. The shortest coverage interval is an interval for which the length is least over all coverage intervals having the same coverage probability. Prior knowledge about the true value of the output quantity Y displaystyle Y can also be considered. For the domestic bathroom scale, the fact that the person's mass is positive, and that it is the mass of a person, rather than that of a motor car, that is being measured, both constitute prior knowledge about the possible values of the measurand in this example. Such additional information can be used to provide a probability distribution for Y displaystyle Y that can give a smaller standard deviation for Y displaystyle Y and hence a smaller standard uncertainty associated with the estimate of Y displaystyle Y .[4][5][6] Type A and Type B evaluation of uncertainty[edit] Knowledge about an input quantity X i displaystyle X_ i is inferred from repeated measured values ("Type A evaluation of uncertainty"), or scientific judgement or other information concerning the possible values of the quantity ("Type B evaluation of uncertainty"). In Type A evaluations of measurement uncertainty, the assumption is often made that the distribution best describing an input quantity X displaystyle X given repeated measured values of it (obtained independently) is a Gaussian distribution. X displaystyle X then has expectation equal to the average measured value and standard deviation equal to the standard deviation of the average. When the uncertainty is evaluated from a small number of measured values (regarded as instances of a quantity characterized by a Gaussian distribution), the corresponding distribution can be taken as a tdistribution.[7] Other considerations apply when the measured values are not obtained independently. For a Type B evaluation of uncertainty, often the only available information is that X displaystyle X lies in a specified interval [ a , b displaystyle a,b ]. In such a case, knowledge of the quantity can be characterized by a rectangular probability distribution[7] with limits a displaystyle a and b displaystyle b . If different information were available, a probability distribution consistent with that information would be used.[8] Sensitivity coefficients[edit] Main article: Sensitivity analysis Sensitivity coefficients c 1 , … , c N displaystyle c_ 1 ,ldots ,c_ N describe how the estimate y displaystyle y of Y displaystyle Y would be influenced by small changes in the estimates x 1 , … , x N displaystyle x_ 1 ,ldots ,x_ N of the input quantities X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N . For the measurement model Y = f ( X 1 , … , X N ) displaystyle Y=f(X_ 1 ,ldots ,X_ N ) , the sensitivity coefficient c i displaystyle c_ i equals the partial derivative of first order of f displaystyle f with respect to X i displaystyle X_ i evaluated at X 1 = x 1 displaystyle X_ 1 =x_ 1 , X 2 = x 2 displaystyle X_ 2 =x_ 2 , etc. For a linear measurement model Y = c 1 X 1 + ⋯ + c N X N , displaystyle Y=c_ 1 X_ 1 +cdots +c_ N X_ N , with X 1 , … , X N displaystyle X_ 1 ,ldots ,X_ N independent, a change in x i displaystyle x_ i equal to u ( x i ) displaystyle u(x_ i ) would give a change c i u ( x i ) displaystyle c_ i u(x_ i ) in y displaystyle y . This statement would generally be approximate for measurement models Y = f ( X 1 , … , X N ) displaystyle Y=f(X_ 1 ,ldots ,X_ N ) . The relative magnitudes of the terms
c i
u ( x i ) displaystyle c_ i u(x_ i ) are useful in assessing the respective contributions from the input quantities to the standard uncertainty u ( y ) displaystyle u(y) associated with y displaystyle y . The standard uncertainty u ( y ) displaystyle u(y) associated with the estimate y displaystyle y of the output quantity Y displaystyle Y is not given by the sum of the
c i
u ( x i ) displaystyle c_ i u(x_ i ) , but these terms combined in quadrature,[1] namely by [an expression that is generally approximate for measurement models Y = f ( X 1 , … , X N ) displaystyle Y=f(X_ 1 ,ldots ,X_ N ) ] u 2 ( y ) = c 1 2 u 2 ( x 1 ) + ⋯ + c N 2 u 2 ( x N ) , displaystyle u^ 2 (y)=c_ 1 ^ 2 u^ 2 (x_ 1 )+cdots +c_ N ^ 2 u^ 2 (x_ N ), which is known as the law of propagation of uncertainty. When the input quantities X i displaystyle X_ i contain dependencies, the above formula is augmented by terms containing covariances,[1] which may increase or decrease u ( y ) displaystyle u(y) .
Uncertainty
defining the output quantity Y displaystyle Y (the measurand), identifying the input quantities on which Y displaystyle Y depends, developing a measurement model relating Y displaystyle Y to the input quantities, and on the basis of available knowledge, assigning probability distributions — Gaussian, rectangular, etc. — to the input quantities (or a joint probability distribution to those input quantities that are not independent). The calculation stage consists of propagating the probability distributions for the input quantities through the measurement model to obtain the probability distribution for the output quantity Y displaystyle Y , and summarizing by using this distribution to obtain the expectation of Y displaystyle Y , taken as an estimate y displaystyle y of Y displaystyle Y , the standard deviation of Y displaystyle Y , taken as the standard uncertainty u ( y ) displaystyle u(y) associated with y displaystyle y , and a coverage interval containing Y displaystyle Y with a specified coverage probability. The propagation stage of uncertainty evaluation is known as the propagation of distributions, various approaches for which are available, including the GUM uncertainty framework, constituting the application of the law of propagation of uncertainty, and the characterization of the output quantity Y displaystyle Y by a Gaussian or a t displaystyle t distribution, analytic methods, in which mathematical analysis is used to derive an algebraic form for the probability distribution for Y displaystyle Y , and a Monte Carlo method,[3] in which an approximation to the distribution function for Y displaystyle Y is established numerically by making random draws from the probability distributions for the input quantities, and evaluating the model at the resulting values. For any particular uncertainty evaluation problem, approach 1), 2) or 3) (or some other approach) is used, 1) being generally approximate, 2) exact, and 3) providing a solution with a numerical accuracy that can be controlled. Models with any number of output quantities[edit] When the measurement model is multivariate, that is, it has any number of output quantities, the above concepts can be extended.[9] The output quantities are now described by a joint probability distribution, the coverage interval becomes a coverage region, the law of propagation of uncertainty has a natural generalization, and a calculation procedure that implements a multivariate Monte Carlo method is available. Alternative perspective[edit] Most of this article represents the most common view of measurement uncertainty, which assumes that random variables are proper mathematical models for uncertain quantities and simple probability distributions are sufficient for representing all forms of measurement uncertainties. In some situations, however, a mathematical interval rather than a probability distribution might be a better model of uncertainty. This may include situations involving periodic measurements, binned data values, censoring, detection limits, or plusminus ranges of measurements where no particular probability distribution seems justified or where one cannot assume that the errors among individual measurements are completely independent.[citation needed] A more robust representation of measurement uncertainty in such cases can be fashioned from intervals.[10][11] An interval [a,b] is different from a rectangular or uniform probability distribution over the same range in that the latter suggests that the true value lies inside the right half of the range [(a + b)/2, b] with probability one half, and within any subinterval of [a,b] with probability equal to the width of the subinterval divided by b – a. The interval makes no such claims, except simply that the measurement lies somewhere within the interval. Distributions of such measurement intervals can be summarized as probability boxes and Dempster–Shafer structures over the real numbers, which incorporate both aleatoric and epistemic uncertainties. See also[edit] Accuracy and precision
Confidence interval
Experimental uncertainty analysis
History of measurement
List of uncertainty propagation software
Propagation of uncertainty
Stochastic measurement procedure
Test method
Uncertainty
Uncertainty
References[edit] ^ a b c JCGM 100:2008. Evaluation of measurement data – Guide to the
expression of uncertainty in measurement, Joint Committee for Guides
in Metrology.
^ Bell, S. Measurement Good Practice Guide No. 11. A Beginner's Guide
to
Uncertainty
Further reading[edit] This article's further reading may not follow's content policies or guidelines. Please improve this article by removing less relevant or redundant publications with the same point of view; or by incorporating the relevant publications into the body of the article through appropriate citations. (December 2014) (Learn how and when to remove this template message) Bich, W., Cox, M. G., and Harris, P. M. Evolution of the "Guide to the
Expression of
Uncertainty
External links[edit] NPLUnc
Estimate of temperature and its uncertainty in small systems, 2011.
Introduction to evaluating uncertainty of measurement
JCGM 200:2008. International Vocabulary of
Metrology
