In this work, we propose a mathematical method for detection of the probable doubtful asphaltene precipitation titration data. The algorithm is performed on the basis of the Leverage approach, in which the statistical Hat matrix, Williams Plot, and the residuals of the model results lead to identify the probable outliers. This method not only contributes to outliers diagnostics but also defines the range of applicability of the applied models and quality of the existing experimental data. Two available scaling equations from the literature are used to pursue the calculation steps. It is found from the obtained results that: I. The applied models to represent/predict the weight percent of asphaltene precipitation are statistically valid and correct. II. All the treated experimental titration data seem to be reliable except one. III. The whole data points present in the dataset are within the domain of applicability of the employed models. (C) 2012 Elsevier Ltd. All rights reserved.