All of the examples above are of non-
negative quantities, but the Gaussian
distribution is
unbounded, and in particular
always attains negative values with non-
zero probability! So all the examples are
wrong, at least in the strict sense.
Whether the Gaussian or Poisson distribution is more common depends on what, exactly, you measure. But it is true that many naturally occurring random variables are approximately Gaussian. This is a consequence of the Central Limit Theorem alluded to above: the average of N iid random variables (which have variance, if you must get technical)) converges a.s. to a Gaussian variable. So if you look at people's heights, they're not normally distributed (since they're always positive). But (presumably due to some underlying stochastic process) it can be modelled with reasonable accuracy as a sum of iid random variables; this, in turn, may be approximated by a normal distribution.
Just don't confuse the pretty mathematical model with what really goes on.
Engineers, Physicists, Statisticians, Computer Scientists, Astronomers, and all the others! Hmmph! I don't know why we allow them to use Mathematics, I really don't...