We describe a methodology for evaluating the statistical performance of information distillation systems and apply it to a simple illustrative example. (An information distiller provides written English responses to English queries based on automated searches/transcriptions/translations of English and foreign-language sources. The sources include written documents and sound tracks.) The evaluation methodology extracts information nuggets from the distiller response texts and gathers them into fuzzy equivalence classes called nugs. Themethodology supports the usual performancemetrics, such as recall and precision, as well as a new information-theoretic metric called proficiency, which measures how much information a distiller provides relative to all of the information provided by a collection of distillers working on a common query and corpora. Unlike previous evaluation techniques, the methodology evaluates the relevance, granularity, and redundancy of information nuggets explicitly.