At present there is no single model of errors that defines the relationship between the various quality assurance methods that have evolved in analytical chemistry. As a result, difficulties are encountered in ensuring that these methods are individually necessary and jointly sufficient. A model is suggested here that supplies that need. This model apportions error between method bias, laboratory bias, run bias and repeatability variation (the so called â ladder of errorsâ ), together with matrix variability. The model allows us to see our current practices in context and to reassess our priorities. In particular, it demonstrates that high-level errors are usually of a magnitude comparable with the more accessible ones but are likely to be overlooked, and suggests ways in which current practices can be improved.