Uncertainties in scientific measurements

All measurements are subject to error. To some extent, measuring instruments have built-in, or inherent, errors, called systematic errors. (For example, a kitchen scale might consistently yield results that are 25 g too high or a thermometer a reading that is 2°C too low.) Limitations in an experimenter s skill or ability to read a scientific instrument also lead to errors and give results that may be either too high or too low. Such errors are called random errors. 

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Most people think of sdence as being a spedfic, exact discipline, with a "correct" answer for every problem. Yet you were introduced to the concept of uncertainty in scientific measurements. What is meant by "uncertainty" How does imcertalnty creep into measurements How is imcertamty indicated in scientific measurements Can imcertainty ever be completely eliminated in experiments Explain. 

Density and Percent Composition Their Use in Problem Solving 1-6 Uncertainties in Scientific Measurements 1-7 Significant Figures... 

This brief excursion Into Decision Theory Is Included to Indicate the manner In which experimental data can be coupled with external (societal) judgments to form a logical basis for societal decisions and actions. A justification for so complex a strategy for decision making is that "simple scientific measurements and model evaluations will always be characterized by measurement uncertainty. Yet societal decisions and actions must take place even under the shadow of uncertainty. For scientific measurements, as discussed in Che following text, however, we shall restrict our attention to the relatively simple Neyman-Pearson hypothesis testing model (8, p. 198). 

Uncertainty on tlie other hand, represents lack of knowledge about factors such as adverse effects or contaminant levels which may be reduced with additional study. Generally, risk assessments carry several categories of uncertainly, and each merits consideration. Measurement micertainty refers to tlie usual eiTor tliat accompanies scientific measurements—standard statistical teclmiques can often be used to express measurement micertainty. A substantial aniomit of uncertainty is often inlierent in enviromiiental sampling, and assessments should address tliese micertainties. There are likewise uncertainties associated with tlie use of scientific models, e.g., dose-response models, and models of environmental fate and transport. Evaluation of model uncertainty would consider tlie scientific basis for the model and available empirical validation. 

Reproductive risk descriptors are intended to address variability of risk within the population and the overall adverse impact on the population. In particular, differences between high-end and central tendency estimates reflect variability in the population but not the scientific uncertainty inherent in the risk estimates. There is uncertainty in all estimates of risk, including reproductive risk. These uncertainties can result from measurement uncertainties, modelling uncertainties and assumptions made due to incomplete data. Risk assessments should address the impact of each of these uncertainties on confidence in the estimated reproductive risk values. 

Present-day analytical laboratories are increasingly under pressure to supply objective evidence of their technical competence, of the reliability of their results and performance, and to seek formal certification or accreditation. This pressure may come from the laboratory s customers (e.g., industry and national bodies) but may also be due to scientific considerations. A QM system in place, validation of methods, uncertainty evaluation, the use of primary standards and CRMs, participation in ILCs, and PT, all serve to assure and demonstrate the quality of measurements. Compared to, say, 30 years ago, the stability of the equipment now available is much improved, and a greater range of RMs for method validation and calibration is accessible. Nevertheless, to achieve mutual (international) acceptance of various bodies of evidence for QA activities, a number of protocols have been developed. The most widely recognized protocols used in chemical measurements and testing are the ISO Guide 9000 2000, ISO/IEC 17025 2005, and OECD Guidelines for GLP, as well as its national and sector equivalents. 

Shylakhter AI (1994) Uncertainty estimates in scientific models Lessons from trends in physical measurements, population and energy projections. In Ayyub BM, Gupta MM, eds. Uncertainty modelling and analysis Theory and applications. Amsterdam, Elsevier Science B.V., pp. 477 196... 

The issues of method validation and assessment of measurement uncertainty in the determination of potentially toxic trace elements in rice are of permanent interest for the scientific community. In this context, the sources of uncertainty associated with the determination of Cd, Cu, Pb, and Zn have been recently estimated in rice through an interlaboratory comparison [30]. Four Brazilian laboratories participated in the proficiency test. The analytical technique used were FAAS, ET-AAS, and ICP-AES. The rice samples were supplied by the Institute for Reference Materials and Measurements (IRMM), Joint Research Center of the European Commission, within the scope of the interlaboratory comparison International Measurement Evaluation Programme (IMEP) 19 Trace Elements in Rice (see also Chapter 7 in this book). Three out of the four laboratories reported values close to the reference values. It was emphasized that, in order to establish a reliable uncertainty budget, all significant sources of uncertainty should be identified. 

The previous scientific committee (CSTEE) said that the uncertainties in the fate of DeBDE warrant risk reduction measures. Today there is further evidence for degradation of this substance to potentially harmful compounds and SCHER also strongly recommends further risk reduction (conclusion iii). Alternatives with properties similar to those of DeBDE should not be used until proven environmentally safe.11... 

In scientific calculations it is also useful to be able to estimate the precision of a procedure that involves several measurements by combining the precisions of the individual steps. That is, we want to answer the following question How do the uncertainties propagate when we combine the results of several different types of measurements There are many ways to deal with the propagation of uncertainty. We will discuss one simple method below. 

During the whole process it is important to involve stakeholders, to ensure that dieir interests and views are correctly understood in the problem identification and problem solution phases. They must also share the conceptual understanding of the system and have confidence in the scientific basis of the assessments of the programme of measures. Furthermore, it is important to evaluate the uncertainty in the different steps and tools used, for instance in the model simulations and in the data collected. Other factors such as social aspects, socioeconomics and restrictions in terms of economy, etc. have to be taken into account in the implementation plan. Stakeholder involvement and uncertainty assessments are not shown as individual steps in die flow chart in Figure 4.1.1, because they should be considered through the whole process. 

In a similar manner, each uncertainty component of Type B is represented by a quantity u., which is obtained from an assumed probability distribution based on all the available information about the measurement process. Since u.is treated like a standard deviation, the standard uncertainty in each Type B component is simply u.. The evaluation of u. is usually based on scientific judgment using all the relevant information available, which may include... 

In reviewing the history of detection limits (in Analytical Chemistry) it is helpful to keep these several, often implicit, differences in mind. If it Is agreed that the concept of detection has meaning, then it is essential that the above questions be fully defined and explicitly addressed. In the view of this author a meaningful approach to analyte detection must be consistent with our approach to uncertainty components of measurement processes and experimental results the soundest approach is probably the last [hypothesis testing] tempered with an appropriate measure of the first [scientific intuition]. 

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