Errors in clinical laboratory tests lead to increased costs and patient risks. Such errors are relatively rare, affecting ∼0.5% of samples. Existing techniques for detecting errors have either far too low sensitivity or specificity to be useful. This preliminary study develops statistical sample selection criteria that capture faults upwards of fifty times more efficiently than expected from random sampling. Although this is only the first step towards an integrated discriminant system for reliable detection of laboratory errors, the statistical detection scheme demonstrated here outperforms existing methods.