Breeders need accurate fineness and maturity results to make important variety decisions. Biased data gives way to erroneous cross-sectional perimeter and wall thickness information. Experimental procedures generally analyze two of the four parameters and the remaining two are calculated. Additionally, the two experimental readings may not be independent of each other. Case studies of bias will be presented along with a method to detect random error and bias, and produce bias-corrected results. The approach is based on the strong relationship between fineness, maturity and micronaire. In effect, a quality control check is run on each sample in the sample set. Additional analyses are not required.