The efficiency of the genotyping process is determined by many simultaneous factors. In actual genotyping, a production run is often preceded by small-scale experiments to find optimal conditions. We propose to use statistical analysis of production run data as well, to gain insight into factors important for the outcome of genotyping. As an example, we show that analysis of variance (ANOVA) applied to the first-pass results of a genetic study reveals important determinants of genotyping success. The largest factor limiting genotyping appeared to be interindividual variation among DNA samples, explaining 20% of the variance, and a smaller reaction volume, sizing failure, and differences among markers all explained similar to10%. Other potentially important factors, such as sample position within the plate and reusing electrophoresis matrix, appeared to be of minor influence. About 55% of the total variance could be explained by systematic factors. These results show that ANOVA can provide valuable feedback to improve genotyping efficiency. We propose to adjust genotype production runs using principles of experimental design in order to maximize genotyping efficiency at little additional cost.