The experimental sulfur content data of various gases are evaluated in this work for outlier diagnostics. The leverage statistical algorithm is applied for this purpose, which includes determination of the statistical Hat matrix, sketching the Williams plot, and calculation of the residuals of selected correlation results. Moreover, the applicability domains of the employed correlation and the quality of the existing experimental data are checked along with the outlier detection. The previously proposed del Valle and Aguilera correlation is applied for representation/prediction of the sulfur content of several gas samples. It is found that the applied correlation for representation of the corresponding solubilities is statistically valid and correct, three data points are not within its applicability domain and one datum is a probable outlier of the evaluated datasets.