ISO 4259-4:2021

Current Revision
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

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ISO 4259-4:2021

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

PUBLISH DATE 2021
ISO 4259-4:2021

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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