ASTM International - ASTM D6708-16
Standard Practice for Statistical Assessment and Improvement of Expected Agreement Between Two Test Methods that Purport to Measure the Same Property of a Material
|Publication Date:||1 January 2016|
|ICS Code (Petroleum products in general):||75.080|
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... View More
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 6: 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 about 95 % confidence.
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 7: 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.View Less
1.1 This practice covers statistical methodology for assessing the expected agreement between two standard test methods that purport to measure the same property of a material, and deciding if a simple linear bias correction can further improve the expected agreement. It is intended for use with results collected from an interlaboratory study meeting the requirement of Practice D6300 or equivalent (for example, ISO 4259). The interlaboratory study must be conducted on at least ten materials that span the intersecting scopes of the test methods, and results must be obtained from at least six laboratories using each method.
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 95 % confidence limit (designated by this practice as the between methods reproducibility) for the difference between two results where each result is obtained by a different operator 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.
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 6.8) 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 95 % confidence limits for 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 methodology outlined in this practice is also applicable for assessing the expected agreement between any two test methods that purport to measure the same property of a material, provided the results are obtained on the same comparison sample set, the standard error associated with each test result is known, the sample set design meets the requirement of this practice, and the statistical degree of freedom of the data set exceeds 30.