When we observe events where the Gaussian distribution does not apply, what does that signify?  Is it noise?  Is it emergence?  Has one shifted scales?  Has one shifted context?  Is it a weak signal of something?  Is there partial dependence or partial correlation?  When the Gaussian distribution is inappropriate as a label, it’s a strong cue. If one learned statistical control, ala Edwards Deming, when Gauss is inappropriate, it’s a signal that there’s noise, and the system is broken.  But this may not be at all what the signal is.  Instead, when the Gaussian distribution fails to hold, it may signal that emergence is occurring, it may signal that the wrong questions are being asked, or it may signal that prior categories are breaking down.  The concept of “model” which we teach managers often fails to convey these lessons.