The ratio of the accuracy (or tolerance) of the unit under test to the accuracy of the reference standard used to calibrate it. TAR is the predecessor to TUR and does not account for measurement uncertainty.
The Test Accuracy Ratio (TAR) is an older metric that compares the accuracy specification of the instrument being calibrated to the accuracy specification of the reference standard. A TAR of 4:1 means the reference standard is four times more accurate than the unit under test. While simple to calculate, TAR has significant limitations because it only compares specifications and ignores the actual measurement uncertainty of the calibration process.
TAR was widely used before the modern uncertainty-based approach became standard practice. It considers only the ratio of the stated accuracies or tolerances of the two instruments, without accounting for environmental effects, repeatability, resolution, or other sources of uncertainty that affect the real-world calibration measurement. Because of these limitations, TAR has been largely superseded by TUR in modern calibration practice.
In calibration management, understanding the difference between TAR and TUR is important because some legacy procedures and specifications still reference TAR. When converting from TAR-based to TUR-based approaches, organizations often find that their actual TUR is significantly less favorable than the TAR suggested, because measurement uncertainty encompasses more error sources than instrument specifications alone. Modern standards like ISO 17025 and ANSI/NCSL Z540.3 require uncertainty-based (TUR) analysis rather than specification-based (TAR) comparisons.
Test accuracy ratio (TAR) is the ratio of the unit-under-test accuracy to the reference standard accuracy. It is a simpler but less rigorous metric than TUR because it does not account for the full measurement uncertainty.
Modern calibration standards (ISO 17025, ANSI/NCSL Z540.3) require TUR, which accounts for full measurement uncertainty. TAR is a legacy metric that may understate the actual risk of false accept decisions.
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