ASTM C1215-1992(2006) Standard Guide for Preparing and Interpreting Precision and Bias Statements in Test Method Standards Used in the Nuclear Industry《核工业用试验方法标准中精密度与及偏倚报告的编写和表达》.pdf
《ASTM C1215-1992(2006) Standard Guide for Preparing and Interpreting Precision and Bias Statements in Test Method Standards Used in the Nuclear Industry《核工业用试验方法标准中精密度与及偏倚报告的编写和表达》.pdf》由会员分享,可在线阅读,更多相关《ASTM C1215-1992(2006) Standard Guide for Preparing and Interpreting Precision and Bias Statements in Test Method Standards Used in the Nuclear Industry《核工业用试验方法标准中精密度与及偏倚报告的编写和表达》.pdf(9页珍藏版)》请在麦多课文档分享上搜索。
1、Designation: C 1215 92 (Reapproved 2006)Standard Guide forPreparing and Interpreting Precision and Bias Statements inTest Method Standards Used in the Nuclear Industry1This standard is issued under the fixed designation C 1215; the number immediately following the designation indicates the year ofor
2、iginal adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon (e) indicates an editorial change since the last revision or reapproval.INTRODUCTIONTest method standards are required to contain precision and
3、bias statements. This guide contains aglossary that explains various terms that often appear in these statements as well as an exampleillustrating such statements for a specific set of data. Precision and bias statements are shown to varyaccording to the conditions under which the data were collecte
4、d. This guide emphasizes that the errormodel (an algebraic expression that describes how the various sources of variation affect themeasurement) is an important consideration in the formation of precision and bias statements.1. Scope1.1 This guide covers terminology useful for the preparationand int
5、erpretation of precision and bias statements.1.2 In formulating precision and bias statements, it isimportant to understand the statistical concepts involved and toidentify the major sources of variation that affect results.Appendix X1 provides a brief summary of these concepts.1.3 To illustrate the
6、 statistical concepts and to demonstratesome sources of variation, a hypothetical data set has beenanalyzed in Appendix X2. Reference to this example is madethroughout this guide.1.4 It is difficult and at times impossible to ship nuclearmaterials for interlaboratory testing. Thus, precision stateme
7、ntsfor test methods relating to nuclear materials will ordinarilyreflect only within-laboratory variation.2. Referenced Documents2.1 ASTM Standards:2E 177 Practice for Use of the Terms Precision and Bias inASTM Test MethodsE 691 Practice for Conducting an Interlaboratory Study toDetermine the Precis
8、ion of a Test Method2.2 ANSI Standard:ANSI N15.5 Statistical Terminology and Notation forNuclear Materials Management33. Terminology for Precision and Bias Statements3.1 Definitions:3.1.1 accuracy (see bias)(1) bias. (2) the closeness of ameasured value to the true value. (3) the closeness of ameasu
9、red value to an accepted reference or standard value.3.1.1.1 DiscussionFor many investigators, accuracy isattained only if a procedure is both precise and unbiased (seebias). Because this blending of precision into accuracy canresult occasionally in incorrect analyses and unclear statementsof result
10、s, ASTM requires statement on bias instead of accu-racy.43.1.2 analysis of variance (ANOVA)the body of statisticaltheory, methods, and practices in which the variation in a set ofdata is partitioned into identifiable sources of variation.Sources of variation may include analysts, instruments,samples
11、, and laboratories. To use the analysis of variance, thedata collection method must be carefully designed based on amodel that includes all the sources of variation of interest. (SeeExample, X2.1.1)3.1.3 bias (see accuracy)a constant positive or negativedeviation of the method average from the corre
12、ct value oraccepted reference value.3.1.3.1 DiscussionBias represents a constant error asopposed to a random error.1This guide is under the jurisdiction of ASTM Committee C26 on Nuclear FuelCycle and is the direct responsibility of Subcommittee C26.08 on Quality Assur-ance, Statistical Applications,
13、 and Reference Materials.Current edition approved Jan. 1, 2006. Published February 2006. Originallyapproved in 1992. Last previous edition approved in 1997 as C 121592(1997).2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For
14、Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.3Available from American National Standards Institute (ANSI), 25 W. 43rd St.,4th Floor, New York, NY 10036, http:/www.ansi.org.4Refer to Form and Style for ASTM Standards, 8th Ed., 1989,
15、ASTM.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.(a) A method bias can be estimated by the difference (orrelative difference) between a measured average and an ac-cepted standard or reference value. The data from which theestimat
16、e is obtained should be statistically analyzed to establishbias in the presence of random error. A thorough bias investi-gation of a measurement procedure requires a statisticallydesigned experiment to repeatedly measure, under essentiallythe same conditions, a set of standards or reference material
17、s ofknown value that cover the range of application. Bias oftenvaries with the range of application and should be reportedaccordingly.(b) In statistical terminology, an estimator is said to beunbiased if its expected value is equal to the true value of theparameter being estimated. (See Appendix X1.
18、)(c) The bias of a test method is also commonly indicated byanalytical chemists as percent recovery. A number of repeti-tions of the test method on a reference material are performed,and an average percent recovery is calculated. This averageprovides an estimate of the test method bias, which is mul
19、ti-plicative in nature, not additive. (See Appendix X2.)(d) Use of a single test result to estimate bias is stronglydiscouraged because, even if there were no bias, random erroralone would produce a nonzero bias estimate.3.1.4 coeffcient of variationsee relative standard devia-tion.3.1.5 confidence
20、intervalan interval used to bound thevalue of a population parameter with a specified degree ofconfidence (this is an interval that has different values fordifferent random samples).3.1.5.1 DiscussionWhen providing a confidence interval,analysts should give the number of observations on which theint
21、erval is based. The specified degree of confidence is usually90, 95, or 99 %. The form of a confidence interval depends onunderlying assumptions and intentions. Usually, confidenceintervals are taken to be symmetric, but that is not necessarilyso, as in the case of confidence intervals for variances
22、.Construction of a symmetric confidence interval for a popula-tion mean is discussed in Appendix X3.It is important to realize that a given confidence-interval estimateeither does or does not contain the population parameter. The degree ofconfidence is actually in the procedure. For example, if the
23、interval (9,13) is a 90 % confidence interval for the mean, we are confident that theprocedure (take a sample, construct an interval) by which the interval(9, 13) was constructed will 90 % of the time produce an interval thatdoes indeed contain the mean. Likewise, we are confident that 10 % ofthe ti
24、me the interval estimate obtained will not contain the mean. Notethat the absence of sample size information detracts from the usefulnessof the confidence interval. If the interval were based on five observa-tions, a second set of five might produce a very different interval. Thiswould not be the ca
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