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[Ryan with input from Eric - a paragraph?]  [Here or elsewhere?] Unlike the study carried out in Koch et al.~(2015), We characterize  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, mock observational data using established quantitative analyses, which  we will focus our discussion on those statistics deemed by Koch denote as "turbulent statistics" (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. 2015).  Table ??? enumerates our astrostatistical toolkit. We classify the established turbulent statistics each statistic  by their methods its method  of analysis. Intensity statistics quantify emission distributions using intensity moment maps. Fourier Statistics distributions; fourier statistics  analyze a 1-dimensional N-dimensional  power spectrum spectra  obtained through spatial integration techniques (established in the literature??). Morphology techniques; morphology  statistics characterize structure and emission properties. A detailed the tatistic can be found in Koch et al. (2015). For each synthetic observation, we calculate the required velocity moment maps and implement each turbulent statistic using the Python package TurbuStat. (Sentence on how our initial input conditions closely follow the default input conditions?). We quantify  [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.