Biases, also known as systematic errors, are commonly found in data-assimilation systems. All system components, including the forecast model, boundary conditions, observations, observation operators, and covariance models, can introduce, extrapolate, or amplify biases. To detect biases, differences between observations and their model-predicted equivalents can be monitored on the input side. At the same time, systematic features of the analysis increments can be examined on the output side. Identifying different sources of bias requires additional information, such as independent observations, knowledge of underlying causes, or hypotheses about the error characteristics of possible sources.