  We examine the $\chi^2$ test for binned, Gaussian samples, including effects
due to the fact that the experimentally available sample standard deviation and
the unavailable true standard deviation have different statistical properties.
For data formed by binning Gaussian samples with bin size $n$, we find that the
expected value and standard deviation of the reduced $\chi^2$ statistic is
\begin{equation} \frac{n-1}{n-3}\pm
\frac{n-1}{n-3}\sqrt{\frac{n-2}{n-5}}\sqrt{\frac{2}{N-1}}, \end{equation} where
$N$ is the total number of binned values. This is strictly larger in both mean
and standard deviation than the value of $1\pm (2/(N-1))^{1/2}$ reported in
standard treatments, which ignore the distinction between true and sample
standard deviation.
