ASTM D6708-16b

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 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 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.

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 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 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.

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, and the sample set design meets the requirements of this practice, in particular that the statistical degree of freedom associated with all standard errors are 30 or greater.