5.1 Users of fire test data often need a quantitative indication of the quality of the data presented in a test report. This quantitative indication is referred to as the “measurement uncertainty”. There are two primary reasons for estimating the uncertainty of fire test results.5.1.1 ISO/IEC 17025 requires that competent testing and calibration laboratories include uncertainty estimates for the results that are presented in a report.5.1.2 Fire safety engineers need to know the quality of the input data used in an analysis to determine the uncertainty of the outcome of the analysis.1.1 This guide covers the evaluation and expression of uncertainty of measurements of fire test methods developed and maintained by ASTM International, based on the approach presented in the GUM. The use in this process of precision data obtained from a round robin is also discussed.1.2 The guidelines presented in this standard can also be applied to evaluate and express the uncertainty associated with fire test results. However, it may not be possible to quantify the uncertainty of fire test results if some sources of uncertainty cannot be accounted for. This problem is discussed in more detail in Appendix X2.1.3 Application of this guide is limited to tests that provide quantitative results in engineering units. This includes, for example, methods for measuring the heat release rate of burning specimens based on oxygen consumption calorimetry, such as Test Method E1354.1.4 This guide does not apply to tests that provide results in the form of indices or binary results (for example, pass/fail). For example, the uncertainty of the Flame Spread Index obtained according to Test Method E84 cannot be determined.1.5 In some cases additional guidance is required to supplement this standard. For example, the expression of uncertainty of heat release rate measurements at low levels requires additional guidance and uncertainties associated with sampling are not explicitly addressed.1.6 This fire standard cannot be used to provide quantitative measures.1.7 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this standard.1.8 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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5.1 Many competent measurement laboratories comply with accepted quality system requirements such as ISO 9001, QS 9000, or ISO 17025. When using standard test methods, the measurement results should agree with those from other similar laboratories within the combined uncertainty limits of the laboratories’ measurement systems. It is for this reason that quality system requirements demand that a statement of the uncertainty of the test results accompany every test result.5.2 Preparation of uncertainty estimates is a requirement for laboratory certification under ISO 17025. This practice describes the procedures by which such uncertainty estimates may be calculated.1.1 This practice describes a protocol to be utilized by measurement laboratories for estimating and reporting the uncertainty of a measurement result when the result is derived from a measurand that has been obtained by spectrophotometry.1.2 This practice is specifically limited to the reporting of uncertainty of color measurement results that are reported as color-differences in ΔE format, even though the measurement itself may be reported in other units such as percent reflectance or transmittance.1.3 The procedures defined here are not intended to be applicable to national standardizing laboratories or transfer laboratories.1.4 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.1.5 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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4.1 This practice provides one way for a laboratory to develop data-based Type A estimates of uncertainty as referred to in Section A22 in Form and Style for ASTM Standards.4.2 Laboratories accredited under ISO/IEC 17025 are required to present uncertainty estimates for their test results. This practice provides procedures that use test results to develop uncertainty estimates for an individual laboratory.4.3 Generally, these test results will be from a single sample of stable and homogeneous material known as a control or check sample.4.4 The true value of the characteristic(s) of the control sample being measured will ordinarily be unknown. However, this methodology may also be used if the control sample is a reference material, in which case the test method bias may also be estimated and incorporated into the uncertainty estimate. Many test methods do not have true reference materials available to provide traceable chains of uncertainty estimation.4.5 This practice also allows for ongoing monitoring of the laboratory uncertainty. As estimates of the level of uncertainty change, possibly as contributions to uncertainty are identified and minimized, revision to the laboratory uncertainty will be possible.AbstractThis practice describes techniques for a laboratory to estimate the uncertainty of a test result using data from test results on a control sample. This practice provides one method for a laboratory to estimate Measurement Uncertainty in accordance with Section A22.3 in Form and Style for ASTM Standards. This practice describes the use of control charts to evaluate the data obtained and presents a special type of control chart to monitor the estimate of uncertainty.This practice provides one way for a laboratory to develop data-based Type A estimates of uncertainty as referred to in Section A22 in Form and Style for ASTM Standards.1.1 This practice describes techniques for a laboratory to estimate the uncertainty of a test result using data from test results on a control sample. This practice provides one method for a laboratory to estimate Measurement Uncertainty in accordance with Section A22.3 in Form and Style for ASTM Standards.1.2 Uncertainty as defined by this practice applies to the capabilities of a single laboratory. Any estimate of uncertainty determined through the use of this practice applies only to the individual laboratory for which the data are presented.1.3 The laboratory uses a well defined and established test method in determining a series of test results. The uncertainty estimated using this practice only applies when the same test method is followed. The uncertainty only applies for the material types represented by the control samples, and multiple control samples may be needed, especially if the method has different precision for different sample types or response levels.1.4 The uncertainty estimate determined by this practice represents the intermediate precision of test results. This estimate seeks to quantify the total variation expected within a single laboratory using a single established test method while incorporating as many known sources of variation as possible.1.5 This practice does not establish error estimates (error budget) attributed to individual factors that could influence uncertainty.1.6 This practice describes the use of control charts to evaluate the data obtained and presents a special type of control chart to monitor the estimate of uncertainty.1.7 The system of units for this standard is not specified. Dimensional quantities in the standard are presented only as illustrations of calculation methods. The examples are not binding on products or test methods treated.1.8 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.1.9 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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4.1 All measurements, including dose measurements, have an associated uncertainty. The magnitude of the measurement uncertainty is important for assessing the quality of the results of the measurement system.4.2 Information on the range of achievable uncertainty values for specific dosimetry systems is given in the ISO/ASTM standards for the specific dosimetry systems. While the uncertainty values given in specific dosimetry standards are achievable, it should be noted that both smaller and larger uncertainty values might be obtained depending on measurement conditions and instrumentation. For more information see also ISO/ASTM 52628.4.3 This guide uses the methodology adopted by the GUM for estimating uncertainties in measurements (see 2.4). Therefore, components of uncertainty are evaluated as either Type A uncertainty or Type B uncertainty.4.4 Quantifying individual components of uncertainty may assist the user in identifying actions to reduce the measurement uncertainty.4.5 Periodically, the uncertainty should be reassessed to confirm the existing estimate. Should changes occur that could influence the existing component estimates or result in the addition of new components of uncertainty, a new estimate of uncertainty should be established.4.6 Although this guide provides a framework for assessing uncertainty, it cannot substitute for critical thinking, intellectual honesty, and professional skill. The evaluation of uncertainty is neither a routine task nor a purely mathematical one; it depends on detailed knowledge of the nature of the measurand and of the measurement method and procedure used. The quality and utility of the uncertainty quoted for the result of a measurement therefore ultimately depends on the understanding, critical analysis, and integrity of those who contribute to the assignment of its value (JCGM 100:2008).1.1 This standard provides guidance on the use of concepts described in the JCGM Evaluation of Measurement Data – Guide to the Expression of Uncertainty in Measurement (GUM) to estimate the uncertainties in the measurement of absorbed dose in radiation processing.1.2 Methods are given for identifying, evaluating and estimating the components of measurement uncertainty associated with the use of dosimetry systems and for calculating combined standard measurement uncertainty and expanded (overall) uncertainty of dose measurements based on the GUM methodology.1.3 Examples are given on how to develop a measurement uncertainty budget and a statement of uncertainty.1.4 This document is one of a set of standards that provides recommendations for properly implementing dosimetry in radiation processing, and provides guidance for achieving compliance with the requirements of ISO/ASTM 52628 related to the evaluation and documentation of the uncertainties associated with measurements made with a dosimetry system. It is intended to be read in conjunction with ISO/ASTM 52628, ISO/ASTM 51261 and ISO/ASTM 52701.1.5 This guide does not address the establishment of process specifications or conformity assessment.1.6 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

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5.1 Appropriate application of this practice should result in an estimate of the test-method’s uncertainty (at any concentration within the working range), which can be compared with data-quality objectives to see if the uncertainty is acceptable.5.2 With data sets that compare recovered concentration with true concentration, the resulting regression plot allows the correction of the recovery data to true values. Reporting of such corrections is at the discretion of the user.5.3 This practice should be used to estimate the measurement uncertainty for any application of a test method where measurement uncertainty is important to data use.1.1 This practice establishes a standard for computing the measurement uncertainty for applicable test methods in Committee D19 on Water. The practice does not provide a single-point estimate for the entire working range, but rather relates the uncertainty to concentration. The statistical technique of regression is employed during data analysis.1.2 Applicable test methods are those whose results come from regression-based methods and whose data are intra-laboratory (not inter-laboratory data, such as result from round-robin studies). For each analysis conducted using such a method, it is assumed that a fixed, reproducible amount of sample is introduced.1.3 Calculation of the measurement uncertainty involves the analysis of data collected to help characterize the analytical method over an appropriate concentration range. Example sources of data include: (1) calibration studies (which may or may not be conducted in pure solvent), (2) recovery studies (which typically are conducted in matrix and include all sample-preparation steps), and (3) collections of data obtained as part of the method’s ongoing Quality Control program. Use of multiple instruments, multiple operators, or both, and field-sampling protocols may or may not be reflected in the data.1.4 In any designed study whose data are to be used to calculate method uncertainty, the user should think carefully about what the study is trying to accomplish and much variation should be incorporated into the study. General guidance on designing studies (for example, calibration, recovery) is given in Appendix X1. Detailed guidelines on sources of variation are outside the scope of this practice, but general points to consider are included in Appendix X2, which is not intended to be exhaustive. With any study, the user must think carefully about the factors involved with conducting the analysis, and must realize that the computed measurement uncertainty will reflect the quality of the input data.1.5 Associated with the measurement uncertainty is a user-chosen level of statistical confidence.1.6 At any concentration in the working range, the measurement uncertainty is plus-or-minus the half-width of the prediction interval associated with the regression line.1.7 It is assumed that the user has access to a statistical software package for performing regression. A statistician should be consulted if assistance is needed in selecting such a program.1.8 A statistician also should be consulted if data transformations are being considered.1.9 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.1.10 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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5.1 Traditional methods for expressing geological uncertainty consist of preparing reliability categories based simply on the distance between drill hole data points, such as the one described by Wood et al. (5) that uses only the drill holes within the coal bed. A major drawback of distance methods is their weak to null association with estimation errors. This practice provides a methodology for effectively assessing the uncertainty in coal resource estimates utilizing stochastic simulation. In determining uncertainty for any coal assessment, stochastic simulation enables consideration of other important factors and information beyond the geometry of drill hole locations, both in and out of the coal bed, including: non-depositional channels, depth of weathering, complexity of seam boundaries, coal seam subcrop projections, and varying coal bed geology for different seams due to fluctuating peat depositional environments. Olea et al. (6) explains in detail the methodology behind this practice and illustrates it with an example.5.2 For multi-seam deposits, uncertainty can be expressed on an individual seam basis as well as an aggregated uncertainty for an entire coal deposit.5.3 The uncertainty is expressed directly in tons of coal. Additionally, this practice allows the statistical analysis to be presented according to widely-accepted conventions, such as percentiles and confidence intervals. For example, there is a 90 % probability that the actual tonnage in place is 314 million metric tons ± 28.8 million metric tons (346 million tons ± 31.7 million tons) of coal.5.4 The results of an uncertainty determination can provide important input into an overall risk analysis assessing the commercial feasibility of a coal deposit.5.5 A company may rank coal resources per block (cell) based on the degree of uncertainty.1.1 This practice covers a procedure for quantitatively determining in-place tonnage uncertainty in a coal resource assessment. The practice uses a database on coal occurrence and applies geostatistical methods to model the uncertainty associated with a tonnage estimated for one or more coal seams. The practice includes instruction for the preparation of results in graphical form.1.2 This document does not include a detailed presentation of the basic theory behind the formulation of the standard, which can be found in numerous publications, with a selection being given in the references (1-3).21.3 This practice should be used in conjunction with professional judgment of the many unique aspects of a coal deposit.1.4 Units—The values stated in SI units are to be regarded as standard. The values given in parentheses after SI units are provided for information only and are not considered standard.NOTE 1: All values given in parentheses after SI units are stated in inch-pound units.1.5 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.1.6 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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4.1 Part A of the “Blue Book,” Form and Style for ASTM Standards, introduces the statement of measurement uncertainty as an optional part of the report given for the result of applying a particular test method to a particular material.4.2 Preparation of uncertainty estimates is a requirement for laboratory accreditation under ISO/IEC 17025. This guide describes some of the types of data that the laboratory can use as the basis for reporting uncertainty.AbstractThis guide provides concepts necessary for understanding the term “uncertainty” when applied to a quantitative test result. Several measures of uncertainty can be applied to a given measurement result; the interpretation of some of the common forms is described. This guide describes methods for expressing test result uncertainty and relates these to standard statistical methodology. Relationships between uncertainty and concepts of precision and bias are described. This guide also presents concepts needed for a laboratory to identify and characterize components of method performance. Elements that an ASTM method can include to provide guidance to the user on estimating uncertainty for the method are described. This guide describes some of the types of data that the laboratory can use as the basis for reporting uncertainty.1.1 This guide provides concepts necessary for understanding the term “uncertainty” when applied to a quantitative test result. Several measures of uncertainty can be applied to a given measurement result; the interpretation of some of the common forms is described.1.2 This guide describes methods for expressing test result uncertainty and relates these to standard statistical methodology. Relationships between uncertainty and concepts of precision and bias are described.1.3 This guide also presents concepts needed for a laboratory to identify and characterize components of method performance. Elements that an ASTM method can include to provide guidance to the user on estimating uncertainty for the method are described.1.4 The system of units for this guide is not specified. Dimensional quantities in the guide are presented only as illustrations of calculation methods and are not binding on products or test methods treated.1.5 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.1.6 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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This guide will evaluate sample data that contain a high level of uncertainty for decision-making purposes and, where it is feasible, design a statistical study to estimate and reduce the sources of uncertainty. Oftentimes, historical data may be available and adequate for this purpose and no new study is needed.3.1.1 This approach will help the stakeholders better understand where the greatest sources of uncertainty are in the sampling and analysis process. Resources can be directed to where they can most reduce the overall uncertainty.3.1.2 Sampling and analysis design under this approach can often be cost-efficient because (a) the reduction in uncertainty can be done by statistical means alone and (b) the reduction can be translated into a lower number of analyses.This guide is limited to the situation where a decision is based on the mean of a population. It will only include discussions of a balanced design for the collection and analysis of sample data in order to estimate the sources of uncertainty. References to unbalanced designs are provided where appropriate.1.1 Waste management decisions generally involve uncertainty because of the fact that decisions are based on the use of sample data. When uncertainty can be reduced or controlled, a better decision can be achieved. One way to reduce or control uncertainty is through the estimation and control of the components contributing to the overall uncertainty (or variance). Control of the sizes of these variance components is an optimization process. The optimizations results can be used to either improve an existing sampling and analysis plan (if it should be found to be inadequate for decision-making purposes) or to optimize a new plan by directing resources to where the overall variance can be reduced the most.1.2 Estimation of the variance components from the total variance starts with the sampling and measurement process. The process involves two different kinds of uncertainties: random and systematic. The former is associated with imprecision of the data, while the latter is associated with bias of the data. This guide will discuss only sources of uncertainty of a random nature.1.3 There may be many sources of uncertainty in waste management decisions. However, this guide does not intend to address the issue of how these sources are identified. It is the responsibility of the stakeholders and their technical staff to analyze the sampling and measurement processes in order to identify the potentially significant sources of uncertainty. After identifying these sources, this guide will provide guidance on how to collect and analyze data to obtain an estimate of the total uncertainty and its components.

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6.1 A primary use intended for this practice is for qualifying ASTM International Standards as Standard Test Methods. In the past, a “Precision and Bias” report has been required. However, recently a statement of uncertainty has become an acceptable alternative to Guide D3670. Inclusion of such a statement with a method description simplifies comparison of ASTM Test Methods to analogous ISO and Committee for European Normalization (CEN) standards, now required to have uncertainty statements.6.2 Standardizing the characterization of sampling/analytical method performance is expected to be useful in other applications as well. For example, performance details are a necessity for justifying compliance decisions based on experimental air quality assessments (7). Documented uncertainty can form a basis for specific criteria defining acceptable sampling/analytical method performance.6.3 Furthermore, high quality atmospheric measurements are vital for making decisions as to how hazardous substances are to be controlled. Valid data are required for drawing reasonable epidemiological conclusions, for making sound decisions as to acceptable limits, as well as for determining the efficacy of a hazard control system.6.4 Finally, because of developing world-wide acceptance of ISO GUM for detailing measurements when statistics are simple, the practice should be useful in comparing ASTM International Test Methods to other published methods. The codification of statistical procedures may in fact minimize the difficulty in interpreting a plethora of individual, albeit possibly valid, approaches.1.1 This practice is for assisting developers and users of air quality methods for sampling concentrations of both airborne and settled materials in characterizing measurements as to uncertainty. Where possible, analysis into uncertainty components as recommended in the International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement (ISO GUM, (1)2) is suggested. Aspects of uncertainty estimation particular to air quality measurement are emphasized. For example, air quality assessment is often complicated by: the difficulty of taking replicate measurements owing to the large spatio-temporal variation in concentration values to be measured; systematic error or bias, both corrected and uncorrected; and the (rare) non-normal distribution of errors. This practice operates mainly through example. Background and mathematical development are relegated to appendices for optional reading.1.2 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this standard.1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.1.4 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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5.1 This guide is intended to help testing laboratories and the developers of methods and software for those laboratories to apply the concepts of measurement uncertainty to radiochemical analyses.5.2 The result of a laboratory measurement never exactly equals the true value of the measurand. The difference between the two is called the error of the measurement. An estimate of the possible magnitude of this error is called the uncertainty of the measurement. While the error is primarily a theoretical concept, since its value is never known, the uncertainty has practical uses. Together, the measured value and its uncertainty allow one to place bounds on the likely true value of the measurand.5.3 Reliable measurement-based decision making requires not only measured values but also an indication of their uncertainty. Traditionally, significant figures have been used with varying degrees of success to indicate implicitly the order of magnitude of measurement uncertainties; however, reporting an explicit uncertainty estimate with each result is more reliable and informative, and is considered an industry-standard best practice.1.1 This guide provides concepts, terminology, symbols, and recommendations for the evaluation and expression of the uncertainty of radiochemical measurements of water and other environmental media by testing laboratories. It applies to measurements of radionuclide activities, including gross activities, regardless of whether they involve chemical preparation of the samples.1.2 This guide does not provide a complete tutorial on measurement uncertainty. Interested readers should refer to the documents listed in Section 2 and References for more information. See, for example, GUM, QUAM, Taylor and Kuyatt (1)2, and Chapter 19 of MARLAP (2).1.3 The system of units for this guide is not specified. Dimensional quantities in the guide are presented only as illustrations of calculation methods. The examples are not binding on products or test methods treated.1.4 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.1.5 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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1.1 This practice describes a model for establishing ISO 17025-compliant uncertainty budgets for the chemical analysis of metals, ores, and related materials. It is based on applying the Horwitz function to widely accepted, diverse interlaboratory test programs, such as interlaboratory testing of standard test methods and proficiency testing programs. This function expresses the interlaboratory standard deviations that can be expected for any concentration level as competent laboratories use optimized test procedures to analyze any matrix for any analyte. It may be used to set aim uncertainties against which to plan new standard test methods and to assess the performance of existing test methods.1.2 An optimized test procedure is one in which the final test results are at least equivalent to alternative, state-of-the-art procedures. In the analytical chemistry community, this means that calibrations are carried out, verified, and controlled such that the final test results have no systematic, detectable bias. The elimination of sources of bias is a key responsibility of any person who designs analytical test methods. Hence, an analytical test method that contains systematic, measurable sources of bias would probably not be accepted as an ASTM test method and its performance data would probably not be in compliance with the procedures described in this practice.1.3 The uncertainty budget model described in this practice is based on the assumption that, in a normally distributed, bias-free environment, measurement uncertainty will improve by the square root of two with each removal of a significant source of variation. Conversely, it is assumed that measurement uncertainty will worsen by the same amount with each addition of a significant source of variation. Furthermore, this model assumes that the hierarchy of increasing variation in any composition-based measurement system begins with calibration and progresses through control to intralaboratory standard deviation to interlaboratory standard deviation to product sampling for conformity assessment. Therefore, aim values for the expected uncertainties at any process step can be predicted using this model.1.4 When using this model, the aim values generated using this model must then be validated, verified, and documented as part of the development and interlaboratory testing of any new test method, sampling practice, and product specification, as appropriate. It is also expected that each laboratory that elects to use that standard test method will generate data to show that the standard test method complies with the published uncertainties developed during interlaboratory testing of the standard test method. The principles in this practice can also be applied to the development of test methods used to determine the composition of other materials.1.5 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

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5.1 The uncertainty in outdoor solar irradiance measurement has a significant impact on weathering and durability and the service lifetime of materials systems. Accurate solar irradiance measurement with known uncertainty will assist in determining the performance over time of component materials systems, including polymer encapsulants, mirrors, Photovoltaic modules, coatings, etc. Furthermore, uncertainty estimates in the radiometric data have a significant effect on the uncertainty of the expected electrical output of a solar energy installation.5.1.1 This influences the economic risk analysis of these systems. Solar irradiance data are widely used, and the economic importance of these data is rapidly growing. For proper risk analysis, a clear indication of measurement uncertainty should therefore be required.5.2 At present, the tendency is to refer to instrument datasheets only and take the instrument calibration uncertainty as the field measurement uncertainty. This leads to over-optimistic estimates. This guide provides a more realistic approach to this issue and in doing so will also assists users to make a choice as to the instrumentation that should be used and the measurement procedure that should be followed.5.3 The availability of the adjunct (ADJG021317)5 uncertainty spreadsheet calculator provides real world example, implementation of the GUM method, and assists to understand the contribution of each source of uncertainty to the overall uncertainty estimate. Thus, the spreadsheet assists users or manufacturers to seek methods to mitigate the uncertainty from the main uncertainty contributors to the overall uncertainty.1.1 This guide provides guidance and recommended practices for evaluating uncertainties when calibrating and performing outdoor measurements with pyranometers and pyrheliometers used to measure total hemispherical- and direct solar irradiance. The approach follows the ISO procedure for evaluating uncertainty, the Guide to the Expression of Uncertainty in Measurement (GUM) JCGM 100:2008 and that of the joint ISO/ASTM standard ISO/ASTM 51707 Standard Guide for Estimating Uncertainties in Dosimetry for Radiation Processing, but provides explicit examples of calculations. It is up to the user to modify the guide described here to their specific application, based on measurement equation and known sources of uncertainties. Further, the commonly used concepts of precision and bias are not used in this document. This guide quantifies the uncertainty in measuring the total (all angles of incidence), broadband (all 52 wavelengths of light) irradiance experienced either indoors or outdoors.1.2 An interactive Excel spreadsheet is provided as adjunct, ADJG021317. The intent is to provide users real world examples and to illustrate the implementation of the GUM method.1.3 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this standard.1.4 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.1.5 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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Application of Uncertainty—Qualitative and quantitative analyses require different approaches, refer to the references for additional information. Analysts shall understand the limitations of qualitative and quantitative determinations and have tools to estimate a value for measurement uncertainty of relevant, but not necessarily all, numerical results. In this regard, efforts should be made to use the vocabulary, symbols, and formatting expressed in documents published by international standardizing organizations such as ISO and ASTM International. An understanding of uncertainty is fundamental to the interpretation and reporting of results. The term “uncertainty” does not imply doubt; rather, its consideration provides assurance that results and conclusions from methods and analytical schemes are fit for purpose. The concept of uncertainty shall be considered for both qualitative and quantitative results. Laboratory management shall ensure that uncertainty be addressed through the provision of training, procedures and documentation. Laboratory management should consider customer requirements, such as a request for qualitative versus quantitative determinations, which influence the assessment of uncertainty. The benefits of understanding and determining uncertainty in this context include: Enhancing confidence through increased understanding of results, Providing a mechanism to express the reliability of results, Enabling the laboratory management and customer to evaluate the fitness for purpose of results, Facilitating the identification of procedural limitations and providing a basis for improvement, and Complying with accreditation requirements.1.1 This practice provides guidance on the concept of uncertainty and its application to the qualitative and quantitative analysis of seized drugs. In this context, uncertainty encompasses limitations of qualitative methods as well as numerical ranges as applied to quantitative analyses. 1.2 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this standard. 1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

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5.1 Investments in long-lived projects such as buildings are characterized by uncertainties regarding project life, operation and maintenance costs, revenues, and other factors that affect project economics. Since future values of these variable factors are generally not known, it is difficult to make reliable economic evaluations.5.2 The traditional approach to project investment analysis has been to apply economic methods of project evaluation to best-guess estimates of project input variables as if they were certain estimates and then to present results in single-value, deterministic terms. When projects are evaluated without regard to uncertainty of inputs to the analysis, decision-makers may have insufficient information to measure and evaluate the risk of investing in a project having a different outcome from what is expected.5.3 Risk analysis is the body of theory and practice that has evolved to help decision-makers assess their risk exposures and risk attitudes so that the investment that is the best bet for them can be selected.NOTE 1: The decision-maker is the individual or group of individuals responsible for the investment decision. For example, the decision-maker may be the chief executive officer or the board of directors.5.4 Uncertainty and risk are defined as follows. Uncertainty (or certainty) refers to a state of knowledge about the variable inputs to an economic analysis. If the decision-maker is unsure of input values, there is uncertainty. If the decision-maker is sure, there is certainty. Risk refers either to risk exposure or risk attitude.5.4.1 Risk exposure is the probability of investing in a project that will have a less favorable economic outcome than what is desired (the target) or is expected.5.4.2 Risk attitude, also called risk preference, is the willingness of a decision-maker to take a chance or gamble on an investment of uncertain outcome. The implications of decision-makers having different risk attitudes is that a given investment of known risk exposure might be economically acceptable to an investor who is not particularly risk averse, but totally unacceptable to another investor who is very risk averse.NOTE 2: For completeness, this guide covers both risk averse and risk taking attitudes. Most investors, however, are likely to be risk averse. The principles described herein apply both to the typical case where investors have different degrees of risk aversion and to the atypical case where some investors are risk taking while others are risk averse.5.5 No single technique can be labeled the best technique in every situation for treating uncertainty, risk, or both. What is best depends on the following: availability of data, availability of resources (time, money, expertise), computational aids (for example, computer services), user understanding, ability to measure risk exposure and risk attitude, risk attitude of decision-makers, level of risk exposure of the project, and size of the investment relative to the institution’s portfolio.1.1 This guide covers techniques for treating uncertainty in input values to an economic analysis of a building investment project. It also recommends techniques for evaluating the risk that a project will have a less favorable economic outcome than what is desired or expected.21.2 The techniques include breakeven analysis, sensitivity analysis, risk-adjusted discounting, the mean-variance criterion and coefficient of variation, decision analysis, simulation, and stochastic dominance.1.3 The techniques can be used with economic methods that measure economic performance, such as life-cycle cost analysis, net benefits, the benefit-to-cost ratio, internal rate of return, and payback.1.4 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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