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Psychological research has revealed that human performance in the face of uncertainty is spotty at best. Moreover, both novices and experts are subject to these kinds of inaccuracies and errors. This poor report card should be particularly distressing to knowledge engineers (KEs) who are confronted with the dilemma that no matter how uncertain knowledge is represented in an expert system, it is suspect if acquired from a human, even a human expert. Those who are trying to automate knowledge acquisition by building intelligent interfaces to knowledge engineering tools cannot be comforted by this news. Their interfaces would have to contain sophisticated and as yet unspecified metaknowledge about these particular human frailties in order to overcome the problem.