and the resulting collection of power spectra can then be averaged together. While correct, such a “square-then-average" procedure results in lower signal-to-noise than a hypothetical “average-then-square" procedure whereby a single power spectrum is formed out of the entire survey. The latter allows the spatial modes of a survey to be averaged together coherently, which allows instrumental noise to be averaged down very quickly. Roughly speaking, if $N$ patches of sky are averaged in a coherent fashion to constrain a particular spatial mode, the noise on the measured mode amplitude averages down as $1/\sqrt{N}$. Squaring this amplitude to form a power spectrum then results in a quicker $1/N$ scaling of noise. In contrast, a “square-then-average" method combines $N$ independent pieces of information after squaring, and thus the power spectrum noise scales more slowly[^2] as $1/\sqrt{N}$. The result is a less sensitive statistic, whether for the diagnosis of systematics or for a cosmological measurement. To be fair, one could recover the lost sensitivity by also computing all cross-correlations between a series of small overlapping patches. However, the necessary geometric adjustments for such high precision mosaicking will likely be computationally wasteful, and it quickly becomes preferable to adopt an approach that incorporates the 