Genetic linkage analysis entails estimating the distance between two genes on a chromosome using genotype information from a sample of individuals. For human pedigree data counting the number of meiotic crossovers or recombination events is impossible due to the lack of complete information. Consequently maximum likelihood methods are used to estimate the recombination frequency in these cases. Since the advent of high resolution genetic maps, errors in genetic linkage data have become more of a problem. Errors can introduce spurious recombinations which increase the map distance and distort linkage maps reducing the power to locate genetic diseases. A general method for detecting errors in pedigree genotype data is presented. Its performance is evaluated with power studies using Monte Carlo methods on simulated data with pedigree structures similar to the CEPH pedigrees and a larger disease pedigree used in the study of idiopathic dilated cardiomyopathy. An investigation of the effect that errors have on the power of locating a disease gene in a proposed linkage study is also presented. The study's results can be used to plan linkage studies which account for error thereby increasing their probability of success. The error detection method and power study results are important tools for performing linkage studies now and in the future which require high resolution maps.