The problem of outlier detection in the multicriteria decision aid (MCDA) field has not been extensively explored in the current literature. This study presents a novel approach to tackle this challenge, based on two key concepts. Firstly, the degree of importance of a preference relation, which utilizes multicriteria preference indices (derived from the PROMETHEE method) to assess the significance level of a preference relation. Secondly, the similarity of alternatives, which uses the degree of importance to evaluate how similar each alternative is to the others. Based on the distribution of these similarities, outliers are identified using either the Interquartile Range (IQR) method or the Standard Deviations (SD) method. The proposed approach is applied to two real-world scenarios: the world happiness ranking problem and the human development index problem.