We propose a new approach to comparing simulated observations that enables us to determine the significance of the underlying physical effects. We utilize the methodology of experimental design, a subfield of statistical analysis, to establish a framework for comparing simulated position-position-velocity data cubes to each other. We propose three similarity metrics based on methods described in the literature: principal component analysis, the spectral correlation function, and the Cramer multi-variate two-sample similarity statistic. Using these metrics, we intercompare a suite of mock observational data of molecular clouds generated from magnetohydrodynamic simulations with varying physical conditions. Using this framework, we show that all three metrics are sensitive to changing Mach number and temperature in the simulation sets, but cannot detect changes in magnetic field strength and initial velocity spectrum. We highlight the shortcomings of one-factor-at-a-time designs commonly used in astrophysics and propose fractional factorial designs as a means to rigorously examine the effects of changing physical properties while minimizing the investment of computational resources.