This paper provides diagnostic tools for examining the role of influen
tial observations in Data Envelopment Analysis (DEA) applications. Obs
ervations may be prioritized for further scrutiny to see if they are c
ontaminated by data errors; this prioritization is important in situat
ions where data-checking is costly and resources are limited. Several
empirical examples are provided using data from previously published s
tudies.