Pinpointing genetic associations in the human genome relies heavily on the accuracy of the underlying genotype data. Null alleles can generate significant inaccuracies in genotype data and can negatively affect the statistical power of a study. Existing quality control (QC) tests, including tests of Hardy-Weinberg equilibrium, are not sensitive enough to detect the presence of even moderately frequent null alleles in the data. We show that direct analysis of raw data from a quantitative genotyping platform can detect up to 75% of null alleles, even at frequencies below the sensitivity of more traditional methods. Detecting unexpected null alleles not only has benefits in QC of genotype data but may also be valuable in detecting rare, functional null alleles that would otherwise be missed.