DOCSLIB.ORG

Scientific Uncertainty and the Political Process Author(S): Dale Jamieson Source: Annals of the American Academy of Political and Social Science, Vol

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Scientific Uncertainty and the Political Process Author(S): Dale Jamieson Source: Annals of the American Academy of Political and Social Science, Vol American Academy of Political and Social Science Scientific Uncertainty and the Political Process Author(s): Dale Jamieson Source: Annals of the American Academy of Political and Social Science, Vol. 545, Challenges in Risk Assessment and Risk Management (May, 1996), pp. 35-43 Published by: Sage Publications, Inc. in association with the American Academy of Political and Social Science Stable URL: http://www.jstor.org/stable/1047890 Accessed: 05/06/2010 19:36 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/action/showPublisher?publisherCode=sage. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Sage Publications, Inc. and American Academy of Political and Social Science are collaborating with JSTOR to digitize, preserve and extend access to Annals of the American Academy of Political and Social Science. http://www.jstor.org ANNALS, AAPSS, 545, May 1996 Scientific Uncertainty and the Political Process By DALEJAMIESON ABSTRACTIn this article, a notion of scientific uncertainty is sketched that is in many ways different from the prevailing view. Scientific uncertainty is not simply an objective value that can be reduced by science alone. Rather,scientific uncertainty is constructed both by science and by society in order to serve certain purposes. Recognizingthe social role of scientific uncertainty will help us to see how many of our problems about risk are deeply cultural and cannot be overcomesimply by the application of more and better science. Dale Jamieson is professor of philosophy at the University of Colorado, Boulder, and adjunct scientist in the Environmental and Societal Impacts Group at the National Center for Atmospheric Research. He received his Ph.D. from the University of North Carolina at Chapel Hill and previously taught at North Carolina State University and the State University ofNew York.He has also held visiting positions at Cornell, Oxford, and Monash University in Australia. He is the editor of five books and has published many articles on ethics, science policy, and environmental philosophy. 35 36 THE ANNALSOF THE AMERICANACADEMY of the most controversial The conventional wisdom about SOMEpublic policy decisions in Ameri- why science is often so ineffective in can society involve risks that are pri- providing solutions to problems with marily understood through scientific important scientific dimensions fo- processes and institutions. The evi- cuses on the role of uncertainty. In dence for climate change, for exam- this view, problems such as climate ple, comes mainly from experiments change are characterizedby high lev- run on highly complex climate mod- els of scientific uncertainty about the els rather than from our everyday likelihood and effects of key events, experience. Other issues with impor- and so partisans of various policies tant scientific dimensions include can use-or misuse-scientific infor- ozone depletion, biodiversity loss, mation and authority for their own acid rain, and exposure to radon and purposes. For example, although the various toxic chemicals. Without sci- weight of scientific evidence suggests ence and scientists, there would be that large-scale emissions of green- little public concern about a wide house gases are likely to change cli- of range important issues. mate, there are so many uncertain- science has been ef- Although very ties about the roles of clouds, carbon fective in these issues into bringing sinks, and various possible feedbacks the it has been public arena, quite that both greenhouse "hawks" and ineffective at solutions. providing "doves"can reasonably enlist science There are a number of views about as an ally while accusing their oppo- this is the case. Over lunch and why nents of misusing The at scientists science.3 only professional meetings, way out of this situation, some argue, often complain about the lack of un- is for uncertainties to be reduced to derstanding or downright perversity the at which science can deter- the of leaders who point on part political mine a rational What is scientific information. On the policy. ignore needed is a new of other generation super- hand, many policy analysts remote fault scientists for to each computers, greater sensing talking and a and more ac- other rather than capability, larger producing "policy- tive research relevant" own community. science.1 My view, In the conventional uncer- which cannot be view, fully developedhere, is seen as an is that the characteristics of sci- tainty objectivequantity very whose value can be reduced in- ence that enable it to have its unique by vesting in more science. While this cultural authority as a knowledge may usefully be thought of as one of producer disable it from bringing several understandings of uncer- public decisions to closure.2 tainty, it is at best simplistic and mis- 1. See, for example, E. S. Rubin, L. B. Lave, and M. G. Morgan, "Keeping Climate 3. The typology of greenhouse "hawks," Research Relevant," Issues in Science and "doves," and "owls" is developed in Michael H. Technology, 8(2):47-55 (1991-92). Glantz, "Politics and the Air Around Us: Inter- 2. I have developed this view more fully in national Policy Action on Atmospheric Pollu- a number of papers. See, for example, "Ethics, tion by Trace Gases," in Societal Responses to Public Policy and Global Warming," Science, Regional Climate Change: Forecasting by Anal- Technology and Human Values, 17(2):139-53 ogy, ed. M. Glantz (Boulder, CO:Westview Press, (1992). 1988), pp. 41-42. SCIENTIFICUNCERTAINTY 37 leading to think of it as the only or the recognition of the chronictoxicity most important one. Rather than be- of DDT.6 ing a cause of controversy,scientific Fallibility looms large with re- uncertainty is often a consequence of spect to many health and environ- controversy.4This suggests that the mental risks. In some cases, we may social world is active in the construc- know that various exposures are as- tion and characterization of uncer- sociated with harms, but we may tainty, and if we want to understand have little idea ofwhat causal mecha- uncertainty, we need to understand nisms are at work. Although the sta- the socialfactors that help to produceit. tistical evidence may be strong enough for some to attribute causal- FALLIBILITY,UNCERTAINTY, ity, even in these cases we may worry ANDINDETERMINISM about the fallibility of such claims. Ourview of the matter may simply be The first in step understanding wrong-not in details, but thor- uncertainty involves distinguishing so. We not even be in a it from some related notions with oughly may position to assess the probability of which it is often confused.5 our The fact of our fal- is often conflated being wrong. Uncertainty is often with relates to libility usually-indeed, fallibility. Fallibility must but it the fact that we could be about be-ignored, constantly wrong the of to which we presents possibility bringing virtually any proposition down an entire edifice of our from the most knowledge. give assent, arises from (for "Iknow how old Uncertainty ignoring homely example, We take various features of I am")to the most exotic (for fallibility. example, a as and focus on other "I know how old the universe is"). problem given dimensions. For it is Fallibility lurks in the backgroundof example, widely scientific claims and moves to the agreed that the case for climate is weakened the fact that foreground when new evidence change by we are uncertain about the effects of comes flooding in that suggests that clouds on the climate The our previous views about some mat- system. solution is more intensive of ter were not just wrong, but deeply study cloud formation and effects. But to and profoundlywrong. The discovery clouds as an area of of the ozone hole, which was not pre- identify uncer- is to dicted by any of the atmospheric tainty presuppose that our gen-- models, is one example of this, as is eral knowledge of the climate system is not uncertain, that the climate 4. This point is argued forcefully in Brian models are and so L. Campbell, "Uncertainty as Symbolic Action basically correct, in Disputes Among Experts," Social Studies of on. This background knowledge is Science, 15:429-53 (1985). "blackboxed"--it is taken as a set of 5. Although I draw the distinctions in a assumptions from which we proceed somewhat different discussion in this way, my to try to reduce uncertainty.This ap- section is indebted to Brian Wynne, "Uncer- tainty and Environmental Learning: Recon- 6. For discussion of these cases, see D. ceiving Science and Policy in the Preventive Budansky, "Scientific Uncertainty and the Pre- Paradigm," Global Environmental Change, cautionary Principle," Environment, 33(7):4-5, 2:111-27 (1992). 43-44 (Sept. 1991). 38 THE ANNALSOF THE AMERICANACADEMY proach of taking some propositionsas or other failings.7 fixed while interrogating others is a Uncertainty should also be distin- fundamental part of scientific prac- guished from indeterminacy.
Load more

Recommended publications
  • What Is the Balance Between Certainty and Uncertainty?
    What is the balance between certainty and uncertainty? Susan Griffin, Centre for Health Economics, University of York Overview • Decision analysis – Objective function – The decision to invest/disinvest in health care programmes – The decision to require further evidence • Characterising uncertainty – Epistemic uncertainty, quality of evidence, modelling uncertainty • Possibility for research Theory Objective function • Methods can be applied to any decision problem – Objective – Quantitative index measure of outcome • Maximise health subject to budget constraint – Value improvement in quality of life in terms of equivalent improvement in length of life → QALYs • Maximise health and equity s.t budget constraint – Value improvement in equity in terms of equivalent improvement in health The decision to invest • Maximise health subject to budget constraint – Constrained optimisation problem – Given costs and health outcomes of all available programmes, identify optimal set • Marginal approach – Introducing new programme displaces existing activities – Compare health gained to health forgone resources Slope gives rate at C A which resources converted to health gains with currently funded programmes health HGA HDA resources Slope gives rate at which resources converted to health gains with currently funded programmes CB health HDB HGB Requiring more evidence • Costs and health outcomes estimated with uncertainty • Cost of uncertainty: programme funded on basis of estimated costs and outcomes might turn out not to be that which maximises health
    [Show full text]
  • Would ''Direct Realism'' Resolve the Classical Problem of Induction?
    NOU^S 38:2 (2004) 197–232 Would ‘‘Direct Realism’’ Resolve the Classical Problem of Induction? MARC LANGE University of North Carolina at Chapel Hill I Recently, there has been a modest resurgence of interest in the ‘‘Humean’’ problem of induction. For several decades following the recognized failure of Strawsonian ‘‘ordinary-language’’ dissolutions and of Wesley Salmon’s elaboration of Reichenbach’s pragmatic vindication of induction, work on the problem of induction languished. Attention turned instead toward con- firmation theory, as philosophers sensibly tried to understand precisely what it is that a justification of induction should aim to justify. Now, however, in light of Bayesian confirmation theory and other developments in epistemology, several philosophers have begun to reconsider the classical problem of induction. In section 2, I shall review a few of these developments. Though some of them will turn out to be unilluminating, others will profitably suggest that we not meet inductive scepticism by trying to justify some alleged general principle of ampliative reasoning. Accordingly, in section 3, I shall examine how the problem of induction arises in the context of one particular ‘‘inductive leap’’: the confirmation, most famously by Henrietta Leavitt and Harlow Shapley about a century ago, that a period-luminosity relation governs all Cepheid variable stars. This is a good example for the inductive sceptic’s purposes, since it is difficult to see how the sparse background knowledge available at the time could have entitled stellar astronomers to regard their observations as justifying this grand inductive generalization. I shall argue that the observation reports that confirmed the Cepheid period- luminosity law were themselves ‘‘thick’’ with expectations regarding as yet unknown laws of nature.
    [Show full text]
  • Climate Scepticism, Epistemic Dissonance, and the Ethics of Uncertainty
    SYMPOSIUM A CHANGING MORAL CLIMATE CLIMATE SCEPTICISM, EPISTEMIC DISSONANCE, AND THE ETHICS OF UNCERTAINTY BY AXEL GELFERT © 2013 – Philosophy and Public Issues (New Series), Vol. 3, No. 1 (2013): 167-208 Luiss University Press E-ISSN 2240-7987 | P-ISSN 1591-0660 [THIS PAGE INTENTIONALLY LEFT BLANK] A CHANGING MORAL CLIMATE Climate Scepticism, Epistemic Dissonance, and the Ethics of Uncertainty Axel Gelfert Abstract. When it comes to the public debate about the challenge of global climate change, moral questions are inextricably intertwined with epistemological ones. This manifests itself in at least two distinct ways. First, for a fixed set of epistemic standards, it may be irresponsible to delay policy- making until everyone agrees that such standards have been met. This has been extensively discussed in the literature on the precautionary principle. Second, key actors in the public debate may—for strategic reasons, or out of simple carelessness—engage in the selective variation of epistemic standards in response to evidence that would threaten to undermine their core beliefs, effectively leading to epistemic double standards that make rational agreement impossible. The latter scenario is aptly described as an instance of what Stephen Gardiner calls “epistemic corruption.” In the present paper, I aim to give an explanation of the cognitive basis of epistemic corruption and discuss its place within the moral landscape of the debate. In particular, I argue that epistemic corruption often reflects an agent’s attempt to reduce dissonance between incoming scientific evidence and the agent’s ideological core commitments. By selectively discounting the former, agents may attempt to preserve the coherence of the latter, yet in doing so they threaten to damage the integrity of evidence-based deliberation.
    [Show full text]
  • Investment Behaviors Under Epistemic Versus Aleatory Uncertainty
    Investment Behaviors Under Epistemic versus Aleatory Uncertainty 2 Daniel J. Walters, Assistant Professor of Marketing, INSEAD Europe, Marketing Department, Boulevard de Constance, 77300 Fontainebleau, 01 60 72 40 00, [email protected] Gülden Ülkümen, Gülden Ülkümen, Associate Professor of Marketing at University of Southern California, 701 Exposition Boulevard, Hoffman Hall 516, Los Angeles, CA 90089- 0804, [email protected] Carsten Erner, UCLA Anderson School of Management, 110 Westwood Plaza, Los Angeles, CA, USA 90095-1481, [email protected] David Tannenbaum, Assistant Professor of Management, University of Utah, Salt Lake City, UT, USA, (801) 581-8695, [email protected] Craig R. Fox, Harold Williams Professor of Management at the UCLA Anderson School of Management, Professor of Psychology and Medicine at UCLA, 110 Westwood Plaza #D511 Los Angeles, CA, USA 90095-1481, (310) 206-3403, [email protected] 3 Abstract In nine studies, we find that investors intuitively distinguish two independent dimensions of uncertainty: epistemic uncertainty that they attribute to missing knowledge, skill, or information, versus aleatory uncertainty that they attribute to chance or stochastic processes. Investors who view stock market uncertainty as higher in epistemicness (knowability) are more sensitive to their available information when choosing whether or not to invest, and are more likely to reduce uncertainty by seeking guidance from experts. In contrast, investors who view stock market uncertainty as higher
    [Show full text]
  • The Flaw of Averages)
    THE NEED FOR GREATER REFLECTION OF HETEROGENEITY IN CEA or (The fLaw of averages) Warren Stevens PhD CONFIDENTIAL © 2018 PAREXEL INTERNATIONAL CORP. Shimmering Substance Jackson Pollock, 1946. © 2018 PAREXEL INTERNATIONAL CORP. / 2 CONFIDENTIAL 1 Statistical summary of Shimmering Substance Jackson Pollock, 1946. Average color: Brown [Beige-Black] Average position of brushstroke © 2018 PAREXEL INTERNATIONAL CORP. / 3 CONFIDENTIAL WE KNOW DATA ON EFFECTIVENESS IS FAR MORE COMPLEX THAN WE LIKE TO REPRESENT IT n Many patients are better off with the comparator QALYS gained © 2018 PAREXEL INTERNATIONAL CORP. / 4 CONFIDENTIAL 2 A THOUGHT EXPERIMENT CE threshold Mean CE Mean CE treatment treatment #1 #2 n Value of Value of Cost per QALY treatment treatment #2 to Joe #1 to Bob © 2018 PAREXEL INTERNATIONAL CORP. / 5 CONFIDENTIAL A THOUGHT EXPERIMENT • Joe is receiving a treatment that is cost-effective for him, but deemed un cost-effective by/for society • Bob is receiving an treatment that is not cost-effective for him, but has been deemed cost-effective by/for society • So who’s treatment is cost-effective? © 2018 PAREXEL INTERNATIONAL CORP. / 6 CONFIDENTIAL 3 HYPOTHETICALLY THERE ARE AS MANY LEVELS OF EFFECTIVENESS (OR COST-EFFECTIVENESS) AS THERE ARE PATIENTS What questions or uncertainties stop us recognizing this basic truth in scientific practice? 1. Uncertainty (empiricism) – how do we gauge uncertainty in a sample size of 1? avoiding false positives 2. Policy – how do we institute a separate treatment policy for each and every individual? 3. Information (data) – how we do make ‘informed’ decisions without the level of detail required in terms of information (we know more about populations than we do about people) © 2018 PAREXEL INTERNATIONAL CORP.
    [Show full text]
  • The Problem of Induction
    The Problem of Induction Gilbert Harman Department of Philosophy, Princeton University Sanjeev R. Kulkarni Department of Electrical Engineering, Princeton University July 19, 2005 The Problem The problem of induction is sometimes motivated via a comparison between rules of induction and rules of deduction. Valid deductive rules are necessarily truth preserving, while inductive rules are not. So, for example, one valid deductive rule might be this: (D) From premises of the form “All F are G” and “a is F ,” the corresponding conclusion of the form “a is G” follows. The rule (D) is illustrated in the following depressing argument: (DA) All people are mortal. I am a person. So, I am mortal. The rule here is “valid” in the sense that there is no possible way in which premises satisfying the rule can be true without the corresponding conclusion also being true. A possible inductive rule might be this: (I) From premises of the form “Many many F s are known to be G,” “There are no known cases of F s that are not G,” and “a is F ,” the corresponding conclusion can be inferred of the form “a is G.” The rule (I) might be illustrated in the following “inductive argument.” (IA) Many many people are known to have been moral. There are no known cases of people who are not mortal. I am a person. So, I am mortal. 1 The rule (I) is not valid in the way that the deductive rule (D) is valid. The “premises” of the inductive inference (IA) could be true even though its “con- clusion” is not true.
    [Show full text]
  • Certainty and Uncertainty in Tax Law: Do Opposites Attract?
    laws Article Certainty and Uncertainty in Tax Law: Do Opposites Attract? Alexander V. Demin Law Institute, Siberian Federal University, 660075 Krasnoyarsk, Russia; [email protected] Received: 14 October 2020; Accepted: 26 November 2020; Published: 4 December 2020 Abstract: The principle of certainty of taxation is the dimension of a general requirement of certainty in the legal system. The purpose of this article is to argue the thesis that uncertainty in tax law is not always an absolute evil, sometimes it acts as a means of the most optimal (and in some cases the only possible) settlement of relations in the field of taxes. On the contrary, uncertainty and fragmentation in tax law are colossal problems subject to overcome by the efforts of scientists, legislators, judges, and practicing lawyers. Uncertainty in tax law is manifested in two ways: on the one hand, negatively—as a defect (omission) of the legislator and, on the other hand, positively—as a set of specific legal means and technologies that are purposefully used in lawmaking and law enforcement. In this context, relatively determined legal tools are an effective channel for transition from uncertainty to certainty in the field of taxation. A tendency towards increased use of relatively determined legal tools in lawmaking processes (for example, principles, evaluative concepts, judicial doctrines, standards of good faith and reasonableness, discretion, open-ended lists, recommendations, framework laws, silence of the law, presumptive taxation, analogy, etc.), and involving various actors (courts, law enforcement agencies and officials, international organizations, citizens, organizations and their associations) allow making tax laws more dynamic flexible, and adequate to changing realities of everyday life.
    [Show full text]
  • WP56: Scientific Uncertainty: a User Guide
    The Munich Re Programme: Evaluating the Economics of Climate Risks and Opportunities in the Insurance Sector Scientific uncertainty: a user’s guide Seamus Bradley August 2011 Centre for Climate Change Economics and Policy Working Paper No. 65 Munich Re Programme Technical Paper No. 9 Grantham Research Institute on Climate Change and the Environment Working Paper No. 56 The Centre for Climate Change Economics and Policy (CCCEP) was established by the University of Leeds and the London School of Economics and Political Science in 2008 to advance public and private action on climate change through innovative, rigorous research. The Centre is funded by the UK Economic and Social Research Council and has five inter-linked research programmes: 1. Developing climate science and economics 2. Climate change governance for a new global deal 3. Adaptation to climate change and human development 4. Governments, markets and climate change mitigation 5. The Munich Re Programme - Evaluating the economics of climate risks and opportunities in the insurance sector (funded by Munich Re) More information about the Centre for Climate Change Economics and Policy can be found at: http://www.cccep.ac.uk. The Munich Re Programme is evaluating the economics of climate risks and opportunities in the insurance sector. It is a comprehensive research programme that focuses on the assessment of the risks from climate change and on the appropriate responses, to inform decision-making in the private and public sectors. The programme is exploring, from a risk management perspective, the implications of climate change across the world, in terms of both physical impacts and regulatory responses.
    [Show full text]
  • A Philosophical Treatise of Universal Induction
    Entropy 2011, 13, 1076-1136; doi:10.3390/e13061076 OPEN ACCESS entropy ISSN 1099-4300 www.mdpi.com/journal/entropy Article A Philosophical Treatise of Universal Induction Samuel Rathmanner and Marcus Hutter ? Research School of Computer Science, Australian National University, Corner of North and Daley Road, Canberra ACT 0200, Australia ? Author to whom correspondence should be addressed; E-Mail: [email protected]. Received: 20 April 2011; in revised form: 24 May 2011 / Accepted: 27 May 2011 / Published: 3 June 2011 Abstract: Understanding inductive reasoning is a problem that has engaged mankind for thousands of years. This problem is relevant to a wide range of fields and is integral to the philosophy of science. It has been tackled by many great minds ranging from philosophers to scientists to mathematicians, and more recently computer scientists. In this article we argue the case for Solomonoff Induction, a formal inductive framework which combines algorithmic information theory with the Bayesian framework. Although it achieves excellent theoretical results and is based on solid philosophical foundations, the requisite technical knowledge necessary for understanding this framework has caused it to remain largely unknown and unappreciated in the wider scientific community. The main contribution of this article is to convey Solomonoff induction and its related concepts in a generally accessible form with the aim of bridging this current technical gap. In the process we examine the major historical contributions that have led to the formulation of Solomonoff Induction as well as criticisms of Solomonoff and induction in general. In particular we examine how Solomonoff induction addresses many issues that have plagued other inductive systems, such as the black ravens paradox and the confirmation problem, and compare this approach with other recent approaches.
    [Show full text]
  • Turns in the Evolution of the Problem of Induction*
    CARL G. HEMPEL TURNS IN THE EVOLUTION OF THE PROBLEM OF INDUCTION* 1. THE STANDARD CONCEPTION: INDUCTIVE "INFERENCE" Since the days of Hume's skeptical doubt, philosophical conceptions of the problem of induction and of ways in which it might be properly solved or dissolved have undergone a series of striking metamor- phoses. In my paper, I propose to examine some of those turnings, which seem to me to raise particularly important questions about the nature of empirical knowledge and especially scientific knowledge. Many, but by no means all, of the statements asserted by empirical science at a given time are accepted on the basis of previously established evidence sentences. Hume's skeptical doubt reflects the realization that most of those indirectly, or inferentially, accepted assertions rest on evidence that gives them no complete, no logically conclusive, support. This is, of course, the point of Hume's obser- vation that even if we have examined many occurrences of A and have found them all to be accompanied by B, it is quite conceivable, or logically possible, that some future occurrence of A might not be accompanied by B. Nor, we might add, does our evidence guarantee that past or present occurrences of A that we have not observed were- or are- accompanied by B, let alone that all occurrences ever of A are, without exception, accompanied by B. Yet, in our everyday pursuits as well as in scientific research we constantly rely on what I will call the method of inductive ac- ceptance, or MIA for short: we adopt beliefs, or expectations, about empirical matters on logically incomplete evidence, and we even base our actions on such beliefs- to the point of staking our lives on some of them.
    [Show full text]
  • Uncertainty Guide
    ICSBEP Guide to the Expression of Uncertainties Editor V. F. Dean Subcontractor to Idaho National Laboratory Independent Reviewer Larry G. Blackwood (retired) Idaho National Laboratory Guide to the Expression of Uncertainties for the Evaluation of Critical Experiments ACKNOWLEDGMENT We are most grateful to Fritz H. Fröhner, Kernforschungszentrum Karlsruhe, Institut für Neutronenphysik und Reaktortechnik, for his preliminary review of this document and for his helpful comments and answers to questions. Revision: 4 i Date: November 30, 2007 Guide to the Expression of Uncertainties for the Evaluation of Critical Experiments This guide was prepared by an ICSBEP subgroup led by G. Poullot, D. Doutriaux, and J. Anno of IRSN, France. The subgroup membership includes the following individuals: J. Anno IRSN (retired), France J. B. Briggs INL, USA R. Bartholomay Westinghouse Safety Management Solutions, USA V. F. Dean, editor Subcontractor to INL, USA D. Doutriaux IRSN (retired), France K. Elam ORNL, USA C. Hopper ORNL, USA R. Jeraj J. Stefan Institute, Slovenia Y. Kravchenko RRC Kurchatov Institute, Russian Federation V. Lyutov VNIITF, Russian Federation R. D. McKnight ANL, USA Y. Miyoshi JAERI, Japan R. D. Mosteller LANL, USA A. Nouri OECD Nuclear Energy Agency, France G. Poullot IRSN (retired), France R. W. Schaefer ANL (retired), USA N. Smith AEAT, United Kingdom Z. Szatmary Technical University of Budapest, Hungary F. Trumble Westinghouse Safety Management Solutions, USA A. Tsiboulia IPPE, Russian Federation O. Zurron ENUSA, Spain The contribution of French participants had strong technical support from Benoit Queffelec, Director of the Société Industrielle d’Etudes et Réalisations, Enghien (France). He is a consultant on statistics applied to industrial and fabrication processes.
    [Show full text]
  • MARLAP Manual Volume III: Chapter 19, Measurement Uncertainty
    19 MEASUREMENT UNCERTAINTY 19.1 Overview This chapter discusses the evaluation and reporting of measurement uncertainty. Laboratory measurements always involve uncertainty, which must be considered when analytical results are used as part of a basis for making decisions.1 Every measured result reported by a laboratory should be accompanied by an explicit uncertainty estimate. One purpose of this chapter is to give users of radioanalytical data an understanding of the causes of measurement uncertainty and of the meaning of uncertainty statements in laboratory reports. The chapter also describes proce- dures which laboratory personnel use to estimate uncertainties. This chapter has more than one intended audience. Not all readers are expected to have the mathematical skills necessary to read and completely understand the entire chapter. For this reason the material is arranged so that general information is presented first and the more tech- nical information, which is intended primarily for laboratory personnel with the required mathe- matical skills, is presented last. The general discussion in Sections 19.2 and 19.3 requires little previous knowledge of statistical metrology on the part of the reader and involves no mathe- matical formulas; however, if the reader is unfamiliar with the fundamental concepts and terms of probability and statistics, he or she should read Attachment 19A before starting Section 19.3. The technical discussion in Sections 19.4 and 19.5 requires an understanding of basic algebra and at least some familiarity with the fundamental concepts of probability and statistics. The discus- sion of uncertainty propagation requires knowledge of differential calculus for a com- Contents plete understanding.
    [Show full text]