deletions | additions       diff --git a/section_Methods_label_methods_subsection__.tex b/section_Methods_label_methods_subsection__.tex  index d36d74e..f13c9f6 100644  --- a/section_Methods_label_methods_subsection__.tex  +++ b/section_Methods_label_methods_subsection__.tex 
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We characterize our synthetic observations through (trying to use "use" less) statistical analysis techniques established in the literature. Table ??? enumerates our astrostatistical toolkit. We classify each statistic by its method of analysis: intensity statistics quantify emission distributions, fourier statistics analyze N-dimensional power spectra obtained through spatial integration techniques, and morphology statistics characterize structure and emission properties. Koch et al. (2015) provides a theoretical description of each turbulent statistic. (2015). For each data cube, we compute the intensity moment maps, and implement the statistical analysis techniques. We use a set of pseudo-distance metrics to quantify statistical differences between synthetic observations, as proposed in Yeremi et al. (2014) and further developed in Koch et al. (2015). To perform all numerical calculations, we use TurbuStat, a Python package containing detailed algorithms for (turbulent/turbulence) statistics and their respective distance metrics.   [Here or elsewhere?] Unlike the study carried out in Koch et al.~(2015), our simulation suite does not utilize experimental design to set the simulation parameter values. As discussed in \citet{Yeremi_2014}, comparisons between outputs in one-factor-at-a-time approaches may give a misleading signal since the statistical effects are not fully calibrated. However, we will focus our discussion on those statistics deemed by Koch et al. (2015) to be ``good", i.e., those which exhibit a response to changes in underlying physical parameters rather than to statistical fluctuations in the data.  ^^^Either Either  here or in our new section 4, where we discuss the distance metric tables