[strainer]

Maybe it goes without saying but what I found distinctive about gaussian distribution in multiple dimensions is that it seems to be the only distribution which produces a smooth radial pattern when plotted co-linearly (yet not radially). All other distributions which I tested exhibit a bias through the main axis when just a number of (variateA,variateB) pairs are plotted. Gaussian seems to be the only one , fundamentally, which shows no sign of the orientation of the axis it is plotted along.

Comes in handy for plotting a radially smooth 'star cluster' without doing polar coordinates and trig. Just plot a load of (x=a_guass,y=another_gaus,z=another_gaus) and you have a radially smooth object. I dont think any other distribution can do that, it seems to me there is something mathematically profound about it which Im sure some mathemagicians have a proper grasp of.

The 'co-linear' distortions of other distributions can be seen here in some plots in the test page for my random distribution lib:

http://strainer.github.io/Fdrandom.js/