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\subsection{Fourier Statistics}  \section{Conclusions}\label{conclude}  We investigated the sensitivity of fifteen commonly applied turbulent statistics to the presence of stellar feedback. The goal of our analysis was to identify whether any of the statistics could serve as a robust indicator of feedback: a smoking gun. Our parameter study was based on magneto-hydrodynamic simulations performed by OA15 with varying magnetic field strengths and degrees of feedback from stellar winds. We first post-processed the simulations with a radiative transfer code to produce synthetic $^{12}$CO(1-0) emission cubes. We then computed fourteen statistical metrics using the python package {\sc turbustat} (K16) and assessed the relative response of each statistic to changes in evolutionary time, magnetic field strength, and stellar mass-loss rate. Here, we focus on only those statistics found by K16 to be ``good", i.e. those which responded to physical changes in parameters but were insensitive to noise fluctuations: intensity PDF, skewness, kurtosis, power spectrum, PCA, SCF, bispectrum, VCA, VCS, $\Delta$-variance, wavelet transform, genus, number of dendrogram features, cramer, and histogram of dendrogram feature intensities. We illustrated each statistic via a comparison between a purely turbulent output and an output with identical turbulence but with embedded stellar sources launching winds (\S3).