At a conference on ‘Strategies to Set Global Analytical Quality Specifications in Laboratory Medicine’ in Stockholm 1999, a hierarchy of models to set analytical quality specifications was decided. The consensus agreement from the conference defined the highest level as ‘evaluation of the effect of analytical performance on clinical outcomes in specific clinical settings’ and the second level as ‘data based on components of biological variation’. Here, the many proposals for analytical quality specifications based on biological variation are examined and the outcomes of the different models for maximum allowable combined analytical imprecision and bias are illustrated graphically. The following models were investigated. (1) The Cotlove et al. (1970) model defining analytical imprecision (%CV_A) in relation to the within-subject biological variation (%CV_w-s) as: %CV_A≤ 0·5 × %CV_W-S (where %CV is percentage coefficient of variation), (2) The Gowans et al. (1988) concept, which defines a functional relationship between analytical imprecision and bias for the maximum allowable combination of errors for the purpose of sharing common reference intervals. (3) The European Group for the Evaluation of Reagents and Analytical Systems in Laboratory Medicine (EGE Lab) Working Group concept, which combines the Cotlove model with the Gowans concept using the maximal acceptable bias. (4) The External Quality Assessment (EQA) Organizers Working Group concept, which is close to the EGE Lab Working Group concept, but follows the Gowans et al. concept of imprecision up to the limit defined by the model of Cotlove et al. (5) The ‘three-level’ concept classifying analytical quality into three levels: optimum, desirable and minimum. The figures created clearly demonstrated that the results obtained were determined by the basic assumptions made. When %CV_W-S is small compared with the population-based coefficient of variation [%CV_p = (%CV²_W-S +%CV²_B-S)^1/2], the EGE Lab and EQA Organizers Working Group concepts become similar. Examples of analytical quality specifications based on biological variations are listed and an application on external quality control is illustrated for plasma creatinine.