The parameter values and assumptions of any economic model are subject to change and error. Sensitivity analysis (SA), broadly defined, is the investigation of these potential changes and errors and their impacts on conclusions to be drawn from the model. There is a very large literature on procedures and techniques for SA, but it includes almost nothing from economists. This paper is a selective review and overview of theoretical and methodological issues in SA. There are many possible uses of SA, described here within the categories of decision support; communication; increased understanding or quantification of the system; and model development. The paper focuses somewhat on decision support. It is argued that even the simplest approaches to SA can be theoretically respectable in decision support if they are applied and interpreted in a way consistent with Bayesian decision theory. This is not to say that SA results should be formally subjected to a Bayesian decision analysis, but that an understanding of Bayesian probability revision will help the modeller plan and interpret a SA. Many different approaches to SA are described, varying in the experimental design used and in the way results are processed. Possible overall strategies for conducting SA are suggested. It is proposed that when using SA for decision support, it can be very helpful to attempt to identify which of the following forms of recommendation is most appropriate: (a) do X, (b) do either X or Y depending on the circumstances, (c) do either X or Y, whichever you like, (d) if in doubt, do X. A system for reporting and discussing SA results is recommended.