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We describe a method of assessing the implications for human error on user interface design of safety-critical systems. In previous work we have proposed a taxonomy of influencing factors that contribute to error. In this article, components of the taxonomy are combined into a mathematical and causal model for error, represented as a Bayesian Belief Net (BBN). TheBBN quantifies error influences arising from user knowledge, ability, and the task environ-ment, combined with factors describing the complexity of user action and user interface quality. The BBN model predicts probabilities of different types of errorslips and mistakes for each component action of a task involving user-system interaction. We propose an Impact Analysis Method that involves running test scenarios against this causal model of error in order to determine user interactions that are prone to different types of error. Applying the proposed method will enable the designer to determine the combinations of influencing factors and their interactions that are most likely to influence human error. Finally we show how such scenario-based causal analysis can be useful as a means of focusing on relevant guidelines for safe user interface design. The proposed method is demonstrated through a case study of an operator performing a task using the control system for a laser spectrophotometer.We describe a method of assessing the implications for human error on user interface design of safety-critical systems. In previous work we have proposed a taxonomy of influencing factors that contribute to error. In this article, components of the taxonomy are combined into a mathematical and causal model for error, represented as a Bayesian Belief Net (BBN). TheBBN quantifies error influences arising from user knowledge, ability, and the task environ-ment, combined with factors describing the complexity of user action and user interface quality. The BBN model predicts probabilities of different types of errorslip for each component action of a task involving user-system interaction. We propose an Impact Analysis Method that involves running test scenarios against this causal model of error in order to determine user interactions that are prone to different types of error. Applying the proposed method will enable the designer to determine the combinations of influencing factors and their interactions that are most likely to influence human error. Finally we show how such scenario-based causal analysis can be useful as a means of focusing on relevant guidelines for safe user interface design. The proposed method is demonstrated through a casestudy of an operator performing a task using the control system for a laser spectrophotometer.