The choice of gate size depends on the type of data association algorithm used and the optimization criteria. A prior well known analysis has presented a practical gate size for tracking approaches that sequentially select the most probable hypothesis. This paper revisits that analysis and presents an alternate approach. The intent of the gate sizing in this paper is to design the largest gate that eliminates any observation from the gate whose hypothesis probability is less than the null hypothesis probability. A study of the two approaches to sizing a gate reveals a dilemma and inconsistencies that under further scrutiny are resolved for reasonable conditions by decomposing the null hypothesis into two hypotheses.