  If the rounding errors are assumed to be distributed independently from the
intrinsic distribution of the random variable, the sample variance $s^2$ of the
rounded variable is given by the sum of the true variance $\sigma^2$ and the
variance of the rounding errors (which is equal to $w^2/12$ where $w$ is the
size of the rounding window). Here the exact expressions for the sample
variance of the rounded variables are examined and it is also discussed when
the simple approximation $s^2=\sigma^2+w^2/12$ can be considered valid. In
particular, if the underlying distribution $f$ belongs to a family of symmetric
normalizable distributions such that $f(x)=\sigma^{-1}F(u)$ where
$u=(x-\mu)/\sigma$, and $\mu$ and $\sigma^2$ are the mean and variance of the
distribution, then the rounded sample variance scales like
$s^2-(\sigma^2+w^2/12)\sim\sigma\Phi'(\sigma)$ as $\sigma\to\infty$ where
$\Phi(\tau)=\int_{-\infty}^\infty{\rm d}u\,e^{iu\tau}F(u)$ is the
characteristic function of $F(u)$. It follows that, roughly speaking, the
approximation is valid for a slowly-varying symmetric underlying distribution
with its variance sufficiently larger than the size of the rounding unit.
