If you want to describe the difference in distribution, perhaps something like a Q-Q plot, or a pair of kernel densities would be common tools, though I assume you have quite discrete looking distributions, in which case that may make something like a pair of barcharts/histograms or a pair of ECDFs better (if possible on a single display)

If you do go with looking at the difference in ECDF, the two-sample Kolmogorov-Smirnov statistic would be the ideal "measure" of significance to go with that, since it's based on the biggest (vertical) difference in ECDF.

If you compare barplots(/histograms) for a discrete distribution, you might like to look at the kinds of measures of discrepancy indicated by smooth tests of goodness of fit (Neyman-Barton type tests), but where the statistic is adapted to two samples. This would in effect correspond to partitioning a chi-square into components associated with orthogonal polynomials, the first of which would correspond to location (a linear term), the second to variance (a quadratic term), and so on (typically you'd look out to order 4 or 6). Books and numerous articles by Rayner and Best (and some others) cover some of that territory.

How to describe the differences in skewed data with same median but statistically different distribution?
I am curious to see a simple demonstration of how special relativity leads to Lorentz Invariance of the Maxwell Equations. Differential form will suffice. 
