Comparing the power of quality-control rules to detect persistent increases in random error.

This paper continues an investigation into the merits of an alternative approach to the statistical evaluation of quality-control rules. In this report, computer simulation is used to evaluate and compare quality-control rules designed to detect increases in within-run or between-run imprecision. When out-of-control conditions are evaluated in terms of their impact on total analytical imprecision, the error detection ability of a rule depends on the relative magnitudes of the between-run and within-run error components under stable operating conditions. A recently proposed rule based on the F-test, designed to detect increases in between-run imprecision, is shown to have relatively poor performance characteristics. Additionally, several issues are examined that have been difficult to address with the traditional evaluation approach.