This study initiates a stream of research on error detection by users of information systems through a laboratory experiment and a field study. The results are applicable to the design and management of information systems and provide the foundation for future research. Existing research suggests that humans are poor detectors of errors in these databases. An exploratory study conducted as the initial phase of this research suggests that these earlier findings and generalizations drawn from them are at least partially incorrect. Interviews with eleven actuaries provide evidence that some users of data successfully detect data errors.
Extensive validation is expensive, imperfect, and sometimes impractical.
Still, if it is true that users are ineffective detectors of errors, investments in extensive data validation may be justified. This research conceptualizes the detection of errors in data as an instance of fault diagnosis. Signal detection theory is used to model performance. Signal detection theory suggests that performance measures should capture both the number of errors correctly detected and the number of accurate data values incorrectly labeled as errors. The findings of the study of actuaries, together with Campbell's theory of individual performance and theories of adaptive decision making, suggest that the base rate of errors, assessments of the payoffs of error detection, and users' goals will influence performance.