This paper extends the two set data association performance model developed by Mori, et al to include miss detections and bias. The referenced paper developed an analytical model for the probability of correct association of two data sets, called 'tracks' and 'measurements,' using an optimal 2 dimensional assignment algorithm, where the 'true' objects are distributed uniformly but at random in a circular disk. For these true objects, measurements are obtained by adding independent random errors with the same covariance. Tracks are obtained in the same way except a different, fixed covariance is used. Finally, one of the data sets includes an additional distribution of random points, considered 'false alarms.' This paper extends their results to obtain an analytical model that accounts for bias between the data sets and missed detections in either data set. The analytical model is useful in assessing the impact of system requirements for sensor sensitivity, random error and inter-sensor bias error on measurement-to- measurement, measurement-to-track or track-to-track association.