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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 find that a variety of statistics exhibit sensitivity to feedback, and we present the following conclusions:  \begin{itemize}  \item The intensity PDF, skewness and kurtosis are each sensitive to the degree of feedback, with strong wind models exhibiting very different distances than week wind models. These showed sensitivity to evolutionary time to a lesser degree but were not not strongly sensitive to magnetic field strength.  \item The PCA showed strong sensitivity to wind strength and weak sensitivity to magnetic field. The covariance matrix in particular exhibited strong peaks at the characteristic wind shell expansion velocity ($v\sim 1-2\kms$), which we predict will be visible in observational data.   \item SCF {\bf I think the slope is different, but need to see the angle average value.}  \item The bispectrum shows less correlation between scales in the case with feedback, which may be the result of the shells reducing magnetic wave propagation and coupling. However, the bispectrum is also sensitive to local conditions, including the sonic and Alf\'venic Mach number, which make absolute identification of feedback challenging.  \item VCS showed a distinct signature of feedback. The transition between the density and velocity-dominated parts of the VCS spectrum occurred at higher velocities and larger scales in the case with winds. This suggests that the breakpoint may encapsulate information about the characteristic scale of feedback. The location of this point depends upon other cloud properties, such as optical depth and the velocity dispersion, however, VCS may be used to compare cloud sub-regions.  \item The genus statistic, which reflects the relative number of peaks and voids, showed sensitivity to feedback at small scales: the number of voids declined when feedback was included. However, the effect was subtle and may not be useful for intercloud comparisons.  \item Both dendrogram statistics showed sensitivity to feedback. In the presence of feedback, the number of features followed a pure power-law rather than following off steeply as in the pure turbulent case. Prior studies find that power-law behavior is not characteristic of any cloud Mach number or magnetic field strength for purely driven turbulence. This suggests the number of features statistic may be a true scale-free metric, which could be used to identify and characterize feedback. The histogram of leaf intensities was broader in the case with feedback, which reflects the larger range of intensities associated with the increased temperatures and densities found in shells. Thus, the intensity feature histogram may be most useful for comparing cloud sub-regions.   \item The power spectrum, VCA, wavelet transform, and $\Delta$-variance show little sensitivity to the presence of feedback aside from an overall offset, which would not be remarkable in comparisons of observational data.  \end{itemize}  In conclusion, our search for a smoking gun was successful. 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.  Although these results provide motivation for optimism, we note several caveats. The simulations 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}.