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EVS-EN ISO 4259-4:2022

Petroleum and related products - Precision of measurement methods and results - Part 4: Use of statistical control charts to validate 'in-statistical-control' status for the execution of a standard test method in a single laboratory (ISO 4259-4:2021)

General information
Valid from 01.07.2022
Base Documents
ISO 4259-4:2021; EN ISO 4259-4:2022
Directives or regulations
None
Standard history
Status
Date
Type
Name
01.07.2022
Main
This document specifies the process and methodology for the construction, operation, and maintenance of statistical control charts to assess if a laboratory's execution of a standard test method is in-statistical-control and how to establish and validate the 'in-statistical-control' status. It specifies control charts that are most appropriate for ISO/TC 28 test methods where the dominant common cause variation is associated with the long term, multiple operator conditions. The control charts specified for determination of in-statistical-control are: individual (I), moving range of 2 (MR2), and either the exponentially weighted moving average (EWMA) or zone-based run rules [similar to Western Electric (WE) run rules[3]] as sensitivity enhancement strategy to support the I-chart. The procedures in this document have been primarily designed for numerical results obtained from testing of control samples prepared from a homogenous source of petroleum and related products in a manner that preserves the homogeneity of properties of interest between control samples. If the test method permits, a certified reference material (CRM) sample is used as a control sample provided the sample composition is representative of the material being tested and is not a pure compound; if this is done then the laboratory best establishes its own mean for the CRM sample. This document is applicable to properties of interest that are (known to be) stable over time, and for data sets with sufficient resolution to support validation of the assumption that the data distribution can be approximately represented by the normal (Gaussian) model. Mitigating strategies are suggested for situations where the assumption cannot be validated.
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