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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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Calibration Error, a Neglected Error Source in the Clinical Laboratory Quality Control.

Atilla Barna Vandra¹

¹Spitalul Clinic Judeţean de Urgenţă, Braşov, Romania (retired).

|December 29, 2025

Summary

Calibration is a measurement, not perfection, and contains inherent errors. Understanding these calibration errors is crucial for accurate quality control and preventing false alarms in laboratory testing.

Keywords:

Bias calibration error calibration graph incorrigible bias quality control random error repeatability conditions reproducibility within laboratory conditions systematic error

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Area of Science:

Clinical Chemistry
Analytical Chemistry
Laboratory Medicine

Background:

Calibration is often perceived as a perfect correction process.
However, all measurements, including calibration, inherently contain errors.
This study investigates the nature and impact of these errors in laboratory settings.

Purpose of the Study:

To identify and quantify sources of calibration errors.
To assess the impact of these errors on laboratory quality control (QC).
To propose improved QC strategies accounting for inherent calibration bias.

Main Methods:

Estimated random calibration error using coefficient of variation of slope factors under repeatability conditions.
Assessed total calibration error by comparing slope factors over one year using identical reagent and calibrator lots.
Analyzed the impact of calibration errors on QC rule sensitivity.

Main Results:

Calibration error exceeds the coefficient of variation measured under repeatability conditions.
A significant portion of calibration bias is inherent and cannot be eliminated.
Existing QC methods, like using the sigma (σ) parameter, can lead to excessive false alarms.

Conclusions:

Calibration is an imperfect measurement process with inherent, incorrigible biases.
Quality control rules must be redesigned to accommodate this bias.
Implementing QC graphs with standard deviation measured in repeatability offers a more accurate solution than traditional methods.