Models for combining random and systematic errors. Assumptions and consequences for different models

A series of models for handling and combining systematic and random variati ons/errors are investigated in order to characterize the different models a ccording to their purpose, their application, and discuss their flaws with regard to their assumptions. The following models are considered 1. linear model, where the random and systematic elements are combined according to a linear concept (TE = \ bias \ + z . sigma), where TE is total error, bias is the systematic error component, sigma is the random error component (sta ndard deviation or coefficient of variation) and z is the probability facto r; 2. squared model with two sub-models of which one is the classical stati stical variance model and the other is the GUM (Guide to Uncertainty in Mea surements) model for estimating uncertainty of a measurement; 3. combined m odel developed for the estimation of analytical quality specifications acco rding to the clinical consequences (clinical outcome) of errors. The consequences of these models are investigated by calculation of the fun ctions of transformation of bias into imprecision according to the assumpti ons and model calculations. As expected, the functions turn out to be rathe r different with considerable consequences for these types of transformatio ns. It is concluded that there are at least three models for combining systemat ic and random variation/errors, each created for its own specific purpose, with its own assumptions and resulting in considerably different results. T hese models should be used according to their purposes.