My position is that it IS important to know the tail areas; a Shewhart chart's false alarm risk can easily be ten times what is expected (0.135% at each tail) for a gamma distribution's upper tail if not even worse. It costs time to have production workers chase false alarms and, if the boy cries "wolf" too many times, it may undermine their confidence in SPC. I stand by my recommendation that the practitioner identify the underlying distribution and set control limits with known false alarm risks.   HOWEVER, this article brings up the idea of synergizing SPC with the costs of quality as discussed in this article. I recommended that the false alarm risk be set at 0.135% at each end but that is only because it is the way Shewhart did it for the normal distribution. Does the false alarm risk HAVE to be 0.135%? Maybe not. A more scientific approach, and it seems that others have looked into this, involves economic design of control charts. For example, http://www.jstor.org/pss/1269598 Another article talks about economic design of control charts using the Taguchi loss function. However, no economic design model will deliver optimum results unless the underlying statistical model is correct. "The Sound of One Tail Flapping" has definitely stimulated my interest in seeing whether a synergy between process economics and use of the correct underlying distribution can be carried even further to overturn existing paradigms about SPC (e.g. the concept of the 0.135% false alarm rate).