Targets may be more likely than non-targets to occur in groups. "Group detection" algorithms exploit this property of target behavior to improve the performance of a detection system. This paper develops some of the issues to be addressed when assessing the performance of a group detection algorithm. Two basic cases are considered, one where object detection is the goal (with group detection as an intermediate tool) and one where group detection is directly the goal. To understand the benefits of group detection algorithms in object detection, we propose considering pre-group to post-group object-level false alarm rate at a fixed detection probability. To understand the relative ease of group detection as an end in itself versus object detection, object-level Receiver Operating Characteristic (ROC) curves may be compared to group-level ROCs. The significance of the assessment approach is demonstrated, where different assessment approaches can produce apparent benefits that differ by several orders-of-magnitude. In addition to the methodology dependence, performance has the usual dependence on operating conditions (OCs), including the target grouping behavior (frequency of group sizes, spatial separation, and mismatch between model and reality), spatial dependence in clutter objects, and the pre-group object-level ROC (which in-turn depends on classical OCs).