Iris recognition is increasingly being deployed on population wide scales for important applications such as border security, social service administration, criminal identification and general population management.  The error rates for this incredibly accurate form of biometric identification are established using well known, laboratory quality datasets.  However, it is has long been acknowledged in biometric theory that not all individuals have the same likelihood of being correctly serviced by a biometric system.  Typically, techniques for identifying clients that are likely to experience a false non-match or a false match error are carried out on a per-subject basis.  This research makes the novel hypothesis that certain ethnical denominations are more or less likely to experience a biometric error.  Through established statistical techniques, we demonstrate this hypothesis to be true and document the notable effect that the ethnicity of the client has on iris similarity scores.  Understanding the expected impact of ethnical diversity on iris recognition accuracy is crucial to the future success of this technology as it is deployed in areas where the target population consists of clientele from a range of geographic backgrounds, such as border crossings and immigration check points.