Many applications of Operational Research in the context of health care involve processes of calibration, validation and sensitivity analysis. Indeed these processes seem to have such an elevated status that their absence is often regarded as a marker that a study is somehow substandard. Undoubtedly this may be the case, however there may also be circumstances where it is perfectly reasonable not to use such methods. This paper concerns general principles underlying mathematical modelling, particularly in contexts where data for calibration are either poor quality or non-existent. The discussion challenges the view that modelling should necessarily be subject to formulaic calibration, validation and sensitivity analysis processes in an attempt to achieve or establish ‘accuracy’. Some models are used purely to deduce the logical consequences of a set of beliefs and in this context, the need for validation is at best questionable. If calibration and sensitivity analysis are to be carried out, there is a need to be clear about what the objective is in such analyses.