BACKGROUND: Cost-effectiveness models are often used to predict the costs and health outcomes that are likely to be associated with various different interventions.  Models are a useful tool for representing the detailed and complex ‘real world’ in a more simple and understandable structure.  Whilst models do not claim to necessarily create an exact replica of the real world, they can be useful in demonstrating the relationships and interactions between various different factors.  However, developers of models often consciously, and unconsciously, make assumptions that are avoidable and may bias the results of a model. METHODS: A review was undertaken on a random selection of published models in different disease areas to aim to identify the frequency of typical ‘errors’ in economic models.  In addition, a simple model was developed and used to explore the relative impact of different types of errors in models.  Each type of error was examined for its likely impact on the model’s overall findings and conclusions.  This helped to gain a greater understanding of both the frequency of different errors and their magnitude of effect.  RESULTS: Mistakes are commonly observed in economic models.  These were often due to limitations in scope of the model, but all were found to be avoidable given unlimited time and data availability.  As well as identifying ‘major’ errors in models, the review also identified many common errors, such as excluding ‘half cycle correction’, that often have very little impact on a model’s results, relative to other common errors. CONCLUSIONS: Whilst many errors in economic models are frequent, many errors often go unnoticed and have significant impact upon a model’s results.  This analysis has highlighted the relative importance of each type of error and has provided suggestions as to how these might be avoided.