Outlier monitoring in geodetic applications is often carried out through the Detection, Identification and Adaptation (DIA) method, whereby an outlier-free null hypothesis and multiple outlier-describing alternative hypotheses are tested against each other, followed by the estimation of the parameters of interest according to the testing decision. In this contribution, we analyse the impact the number of alternative hypotheses has on the quality of the DIA-estimator. It will be proven that the probability of correct identification of an outlier is a non-increasing function of the number of alternative hypotheses. Using two examples, we present numerical analysis of the DIA-estimator’s quality by examining two scenarios: one where all alternatives are considered and another where one of them is excluded.