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 $σ^2$ 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 $s^2=σ^2+w^2/12$ can be considered valid. In particular, if the underlying distribution $f$ belongs to a family of symmetric normalizable distributions such that $f(x)=σ^{-1}F(u)$ where $u=(x-μ)/σ$, and $μ$ and $σ^2$ are the mean and variance of the distribution, then the rounded sample variance scales like $s^2-(σ^2+w^2/12)\simσΦ'(σ)$ as $σ\to\infty$ where $Φ(τ)=\int_{-\infty}^\infty{\rm d}u\,e^{iuτ}F(u)$ 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.