The quality of fisheries data has great impacts on the quality of stock assessment, and thus fisheries management. In this paper, using a case study I evaluate the impacts of two types of error, biased error and atypical error, that can negatively affect the quality of fisheries data in stock assessment. These errors are commonly associated with fisheries data, and assumptions on their sources and statistical properties can have great impacts on the outcome of stock assessment. Although the sources and statistical properties of these errors differed, both of them could result in errors in stock assessment if estimation methods are not appropriate. Different statistical approaches used in fitting models differ in their robustness with respect to errors of different statistical properties in data. This study showed the importance of evaluating the quality of input data and the possibility of developing an approach that is robust to errors in data. Considering the likelihood of fisheries data being affected by errors of different statistical properties, I suggest that the robustness of a stock assessment be evaluated with respect to data quality.