Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to parameter estimation. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with very simplistic and statistically unsound ad hoc methods, involving naive intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these rudimentary procedures, suggesting some simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide physicists with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden.
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