The Q/Hampel method is much more robust than the A+S method. This means that its results are much more reliable in the presence of outliers.
Example: As you can see in the following two charts, both methods yield very similar means and limits of tolerance. Accordingly, the four outliers (labs 33, 37, 27 and 20) lie outside the tolerance limits as computed by both methods.Note how in the case of A+S, the four previously penalized outliers now lie within the tolerance limits. One single alteration is enough to throw the A+S computations off kilter, resulting in a completely different performance assessment for these four labs. In the case of Q/Hampel, however, the tolerance limits – and the mean – have barely shifted. The four previously penalized labs still lie outside the tolerance limits.
Q/Hampel has not been fooled:
Let us see what happens when we change one single lab result:
As you can see in the following two charts, lab 42 is now out on a limb, its solitary score of 73 straying far afield even compared to the 4 outlying results taken over from the previous example. Note how in the case of A+S, the four previously penalized outliers now lie within the tolerance limits. One single alteration is enough to throw the A+S computations off kilter, resulting in a completely different performance assessment for these four labs. In the case of Q/Hampel, however, the tolerance limits – and the mean – have barely shifted. The four previously penalized labs still lie outside the tolerance limits. Q/Hampel has not been fooled.
