Abstract: The least-squares (LS) adjustment approach is very susceptive to outliers. A comparative analysis of statistical tests used for detection and identification of outliers, including Gaussian normal test, Tau test and Student’s t-test, were addressed in details. Both the standardized local sensitivity indicator and the standardized LS residual can be served as Gaussian normal test statistics for outlier detection. However, the former is superior to the latter one for correlated observations in the sense of detection power. When the variance factor is not known, the internally Studentized residual and externally Studentized residual can be employed to perform Tau test and Student’s t-test, respectively. In a parallel manner, the internally Studentized local sensitivity indicator and externally Studentized local sensitivity indicator were constructed and investigated. Since the probability of not rejecting the null hypothesis when it is false (type Ⅱ error) may be too high by using Student’s t-test, and since the Tau test has a limitation in itself, both of them are not appropriate statistical tests. To circumvent this difficulty, the standard deviation involved in the standardized local sensitivity indicator can be replaced by its normalized robust least median of squares estimator, and then perform the Gaussian normal test for detection and identification of outlying observations.