There are three termination criteria commonly used for CCTs. [[Bayesian]] decision theory]] methods offer great flexibility by presenting an infinite choice of loss/utility structures and evaluation considerations, but also introduce greater arbitrariness. A [[confidence interval]] approach calculates a confidence interval around the examinee's current theta estimate at each point in the test, and classifies the examinee when the interval falls completely within a region of theta that defines a classification. This was originally known as adaptive mastery testing (Kingsbury & Weiss, 1983), but does not necessarily require adaptive item selection, nor is it limited to the two-classification mastery testing situation. The [[sequential probability ratio test]] (Reckase, 1983) defines the classification problem as a [[hypothesis test]] that the examinee's theta is equal to a specified point above the cutscore or a specified point below the cutscore.
