Accuracy evaluation basic principles

Accuracy (absence of systematic errors) and uncertainty (coefficient of variation or confidence interval) as caused by random errors and random variations in the procedure are the basic parameters to be considered when discussing analytical results. As stressed in the introduction, accuracy is of primary importance however, if the uncertainty in a result is too high, it cannot be used for any conclusion concerning, e.g. the quality of the environment or of food. An unacceptably high uncertainty renders the result useless. When evaluating the performance of an analytical technique, all basic principles of calibration, of elimination of sources of contamination and losses, and of correction for interferences should be followed (Prichard, 1995). 

Four basic hallmarks of data quality—appropriateness, accuracy, integrity and transparency—are introduced in this document. These hallmarks of data quality represent basic principles that are applicable in many analytical endeavours but are tailored here to the needs of exposure assessment. Through the introduction of these hallmarks, this document provides a common vocabulary and set of qualitative criteria for use in the design, evaluation and use of exposure assessments to support decisions. This general guidance is intended for... 

This is exactly the moment when we have to introduce some kind of distance between two data sets that will help us to evaluate the accuracy of the inverse problem solution. In other words, we need to introduce a geometry to measure the distance between the actual and predicted data. The mathematical theory of function spaces provides us with guidance to the solution of this problem. The basic principles of this... 

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