1.1 本规程涵盖了用于评估两种不同标准测试方法之间预期一致性的统计方法，这两种标准测试方法旨在测量材料的相同性质，并用于决定简单的线性偏差校正是否可以进一步提高预期一致性。它旨在与符合实践要求的实验室间研究结果一起使用 D6300 或等效（例如，ISO 4259). 实验室间研究应在至少十种共同材料上进行，这些材料跨越了试验方法的交叉范围，并且应使用每种方法从至少六个实验室获得结果。 应满足本规范中的要求，以便认为评估适合以任何一种方法发布，如果此类发布包括声称已按照本规范进行的声明。任何此类出版物应包括本规范报告部分规定的有关评估结果某些细节的强制性信息。 1.2 统计方法的前提是不需要进行偏差校正。在没有强有力的统计证据表明偏差校正会使两种方法更好地一致的情况下，不进行偏差校正。 如果需要偏差校正，则 简约原则 从而简单的校正比更复杂的校正更受青睐。 注1: 不遵守简约原则通常会导致模型过于拟合，在实践中表现不佳。 1.3 这种做法的偏差校正仅限于常数校正、比例校正或线性（比例+常数）校正。 1.4 这种做法的偏差校正方法是方法对称的，因为无论哪种方法是偏差，都可以获得等效校正- 校正以匹配另一个。 1.5 提出了一种确定数值极限的方法（本规程指定为 方法间再现性 )将超过约5 % 两个结果之间差异的时间（从长远来看，每20种情况中就有一种），其中每个结果都是由不同实验室的不同操作员使用不同的设备获得的，并且每个操作员都应用两种方法中的一种 十、 和 Y 在相同的材料上，其中一种方法已根据本规程进行了适当的偏差校正，在两种测试方法的正常和正确操作中。 注2: 在本标准实施规程的早期版本中，使用术语“跨方法再现性”代替术语“方法间再现性”。之所以进行更改，是因为“方法间重现性”术语更直观，不易混淆。需要注意的是，这两个术语是同义的，并且可以相互替换，特别是在“跨方法再现性”术语随后在方法中以名称引用的情况下 D6708 在采用本标准实践中的术语变更之前，进行了评估。 注3: 提醒用户不要将根据本规程计算出的方法间再现性应用于成分与实际研究明显不同的材料，因为本规程检测和解决样品特定偏差的能力（见 6.7 )取决于为实验室间研究选择的材料。当存在样品特异性偏差时，样品的类型和范围可能需要从本实践中规定的最小10个大幅扩大，以获得更全面、更可靠的方法间再现性，充分覆盖样品的范围- 不同类型材料的特定偏差。 1.6 本规程适用于测量石油或石油产品定量（数值）特性的试验方法。 1.7 本规程的统计计算也适用于评估两种不同测试方法之间的预期一致性，这两种测试方法旨在使用与 1.1 ，前提是每种测试方法的结果和相关统计数据都是从专门设计的多实验室研究或能力测试项目中获得的（e。 g.:ILCP），其中对于每个样品，每个实验室为每个测试方法提供单个结果。比较样品集应包括至少十种不同的材料，这些材料应跨越试验方法的交叉范围，且材料不得超过实践中的杠杆要求 D6300 .结果和统计应符合 1.7.1 .应满足本惯例中的要求，以便认为评估适合以任何一种方法发布，如果该发布包括声称已按照本惯例进行的声明。任何此类出版物应包括本规范报告部分规定的有关评估某些细节的强制性信息。 R XY 应基于已公布的方法再现性。 1.7.1 对于每个测试方法和样本，用于在中进行评估的结果和统计数据 1.7 应满足以下要求: 1. 结果数量（N） ≥ 10, 2. Anderson-DDarling统计 ≤ 1.12（基于正态分布）， 3. 标准误差（se 样品 )使用在样品平均值N和因子2.8下评估的公布再现性计算如下: 4. 东南方 样品 在数值上小于[R 酒吧 /（2.8√10）]，以及 5. 样品标准偏差 样品 )每均方根技术在统计上不大于R 酒吧 /2.8至少80 % 基于使用30作为R的假定自由度的F检验的比较数据集中的样本的 酒吧 ，和（N−1）表示s 样品 显著性水平为0.05。 1.8 该实践中的方法也可用于在两个变量（X，Y）之间进行线性回归分析，其中两个变量都存在已知的不确定性，这些不确定性在回归范围内可能是恒定的，也可能不是恒定的。用于描述这类线性回归的常用缩写词是ReXY（X和Y中有误差的回归）。如本实践所述，用于评估两个变量之间相关性的ReXY技术可用于可能无法满足严格数据输入要求的调查应用，但其结果仍可用于预期应用。 ReXY的使用应在熟悉本实践中描述的统计理论和技术、与回归分析所用结果的产生和收集相关的方法以及与预期应用相关的评估结果解释的主题专家的指导下进行。 1.9 本国际标准是根据世界贸易组织技术性贸易壁垒委员会发布的《关于制定国际标准、指南和建议的原则的决定》中确立的国际公认的标准化原则制定的。 ====意义和用途====== 5.1 这种做法可用于确定恒定、比例或线性偏差校正是否可以提高两种旨在测量材料相同性能的方法之间的一致性。 5.2 本实践中开发的偏差校正可以应用于单个结果( 十、 )从一种测试方法（方法 十、 )以获得 预测 后果 Y ^ )对于其他测试方法（方法 Y ). 注5: 提醒用户确保 Y ^ 在方法的范围内 Y 在使用之前。 5.3 通过该实践建立的方法间再现性可用于构建约 Y ^ 将包含测试方法的结果 Y ，如果进行的话，大约有95 % 可能性 5.4 本规程可用于指导商业协议和产品处置决策，这些协议和决策涉及根据本规程相互评估的测试方法。 5.5 统计上可检测的偏差的大小与实验研究统计数据的不确定性直接相关。这些不确定性与数据集的大小和所研究过程的精度有关。大数据集或高精度测试方法，或两者兼而有之，可以将实验统计的不确定性降低到“统计上可检测的”偏差可能变得“微不足道”的程度，或者在所研究的测试方法的预期使用中被认为没有实际后果。 因此，建议本实践的用户在执行本实践之前，提前确定偏差校正的幅度，低于该幅度，他们会认为这是不必要的，或者对预期应用没有实际意义。 注6: 应该注意的是，确定这个没有实际问题的最小偏差不是一个统计决定，而是一个直接取决于用户应用要求的主观决定。

1.1 This practice covers statistical methodology for assessing the expected agreement between two different standard test methods that purport to measure the same property of a material, and for the purpose of deciding if a simple linear bias correction can further improve the expected agreement. It is intended for use with results obtained from interlaboratory studies meeting the requirement of Practice D6300 or equivalent (for example, ISO 4259). The interlaboratory studies shall be conducted on at least ten materials in common that among them span the intersecting scopes of the test methods, and results shall be obtained from at least six laboratories using each method. Requirements in this practice shall be met in order for the assessment to be considered suitable for publication in either method, if such publication includes claim to have been carried out in compliance with this practice. Any such publication shall include mandatory information regarding certain details of the assessment outcome as specified in the Report section of this practice. 1.2 The statistical methodology is based on the premise that a bias correction will not be needed. In the absence of strong statistical evidence that a bias correction would result in better agreement between the two methods, a bias correction is not made. If a bias correction is required, then the parsimony principle is followed whereby a simple correction is to be favored over a more complex one. Note 1: Failure to adhere to the parsimony principle generally results in models that are over-fitted and do not perform well in practice. 1.3 The bias corrections of this practice are limited to a constant correction, proportional correction, or a linear (proportional + constant) correction. 1.4 The bias-correction methods of this practice are method symmetric, in the sense that equivalent corrections are obtained regardless of which method is bias-corrected to match the other. 1.5 A methodology is presented for establishing the numerical limit (designated by this practice as the between methods reproducibility ) that would be exceeded about 5 % of the time (one case in 20 in the long run) for the difference between two results where each result is obtained by a different operator in a different laboratory using different apparatus and each applying one of the two methods X and Y on identical material, where one of the methods has been appropriately bias-corrected in accordance with this practice, in the normal and correct operation of both test methods. Note 2: In earlier versions of this standard practice, the term “cross-method reproducibility” was used in place of the term “between methods reproducibility.” The change was made because the “between methods reproducibility” term is more intuitive and less confusing. It is important to note that these two terms are synonymous and interchangeable with one another, especially in cases where the “cross-method reproducibility” term was subsequently referenced by name in methods where a D6708 assessment was performed, before the change in terminology in this standard practice was adopted. Note 3: Users are cautioned against applying the between methods reproducibility as calculated from this practice to materials that are significantly different in composition from those actually studied, as the ability of this practice to detect and address sample-specific biases (see 6.7 ) is dependent on the materials selected for the interlaboratory study. When sample-specific biases are present, the types and ranges of samples may need to be expanded significantly from the minimum of ten as specified in this practice in order to obtain a more comprehensive and reliable between methods reproducibility that adequately cover the range of sample-specific biases for different types of materials. 1.6 This practice is intended for test methods which measure quantitative (numerical) properties of petroleum or petroleum products. 1.7 The statistical calculations of this practice are also applicable for assessing the expected agreement between two different test methods that purport to measure the same property of a material using results that are not as described in 1.1 , provided the results and associated statistics from each test method are obtained from a specifically designed multi-lab study or from a proficiency testing program (e.g.: ILCP) where for each sample a single result is provided by each lab for each test method. The comparison sample set shall comprise at least ten different materials that span the intersecting scopes of the test methods with no material exceeding the leverage requirement in Practice D6300 . Results and statistics shall meet requirements in 1.7.1 . Requirements in this practice shall be met in order for the assessment to be considered suitable for publication in either method, if such publication includes claim to have been carried out in compliance with this practice. Any such publication shall include mandatory information regarding certain details of the assessment as specified in the Report section of this practice. R XY shall be based on the published reproducibility of the methods. 1.7.1 For each test method and sample, results and statistics used to perform the assessment in 1.7 shall meet the following requirements: (1) No. of results (N) ≥ 10, (2) Anderson Darling statistic ≤ 1.12 (based on Normal Distribution), (3) Standard Error (se sample ) is calculated using published reproducibility evaluated at the sample mean, N, and the factor 2.8 as follows: (4) se sample is numerically less than [R pub / (2.8 √10 )], and (5) Sample standard deviation (s sample ) per root-mean-square technique is not statistically greater than R pub / 2.8 for at least 80 % of the samples in the comparison data set based on an F-test using 30 as the assumed degrees of freedom for R pub , and (N − 1) for s sample at the 0.05 significance level. 1.8 The methodology in this practice can also be used to perform linear regression analysis between two variables (X, Y) where there is known uncertainty in both variables that may or may not be constant over the regression range. The common acronym used to describe this type of linear regression is ReXY (Regression with errors in X and Y). The ReXY technique for assessing the correlation between two variables as described in this practice can be used for investigative applications where the strict data input requirement may not be met, but the outcome can still be useful for the intended application. Use of this practice for ReXY should be conducted under the tutelage of subject matter experts familiar with the statistical theory and techniques described in this practice, the methodologies associated with the production and collection of the results to be used for the regression analysis, and interpretation of assessment outcome relative to the intended application. 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. ====== Significance And Use ====== 5.1 This practice can be used to determine if a constant, proportional, or linear bias correction can improve the degree of agreement between two methods that purport to measure the same property of a material. 5.2 The bias correction developed in this practice can be applied to a single result ( X ) obtained from one test method (method X ) to obtain a predicted result ( Y ^ ) for the other test method (method Y ). Note 5: Users are cautioned to ensure that Y ^ is within the scope of method Y before its use. 5.3 The between methods reproducibility established by this practice can be used to construct an interval around Y ^ that would contain the result of test method Y , if it were conducted, with approximately 95 % probability. 5.4 This practice can be used to guide commercial agreements and product disposition decisions involving test methods that have been evaluated relative to each other in accordance with this practice. 5.5 The magnitude of a statistically detectable bias is directly related to the uncertainties of the statistics from the experimental study. These uncertainties are related to both the size of the data set and the precision of the processes being studied. A large data set, or, highly precise test method(s), or both, can reduce the uncertainties of experimental statistics to the point where the “statistically detectable” bias can become “trivially small,” or be considered of no practical consequence in the intended use of the test method under study. Therefore, users of this practice are advised to determine in advance as to the magnitude of bias correction below which they would consider it to be unnecessary, or, of no practical concern for the intended application prior to execution of this practice. Note 6: It should be noted that the determination of this minimum bias of no practical concern is not a statistical decision, but rather, a subjective decision that is directly dependent on the application requirements of the users.