  In computational materials science, mechanical properties are typically
extracted from simulations by means of analysis routines that seek to mimic
their experimental counterparts. However, simulated data often exhibit
uncertainties that can propagate into final predictions in unexpected ways.
Thus, modelers require data analysis tools that (i) address the problems posed
by simulated data, and (ii) facilitate uncertainty quantification. In this
manuscript, we discuss three case studies in materials modeling where careful
data analysis can be leveraged to address specific instances of these issues.
As a unifying theme, we highlight the idea that attention to physical and
mathematical constraints surrounding the generation of computational data can
significantly enhance its analysis.
