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We investigated the sensitivity of fourteen 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, 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).   We then computed the distance metric, as defined for each statistic by K16, for each pair of outputs (\S4). This allowed us to both quantify changes and simply the comparison by reducing each pair to one characteristic number. We found find that  a variety of statistical signatures associated with feedback. statistics exhibit sensitivity to feedback, and we present the following conclusions:  \begin{itemize}  \item  \item  \end{itemize}  On the basis of these results, we recommend follow-up observational studies focusing on active star-forming regions utilizing PCA, SCF, VCS, genus, and dendrograms.  The simulations neglected neglect  gravity, which should be considered in future work. We caution that many statistics presented here have two or more distinct definitions in the literature. Our conclusions hold only for the definitions stated in K16. Additional studies are needed to check alternative statistical conventions. Finally, we note that the results are sensitive to the line optical depth \citep{lazarian04,burkhart13a} and tracers with different optical depth and chemistry may yield different results \citep[e.g, ][]{swift08,gaches15}.