On the website (this link): Femur: Group Difference Statistics in Python  ShapeWorks (sciinstitute.github.io)
“For the femur mode, separate statistical tests for differences in correspondence positions are applied to every correspondence index. We use Hotelling T^2 metric(nonparametric permutation test) with false discovery rate correction (FDR) for multiple comparisons. This method helps identify and visualize localized regions of significant shape differences. The null hypothesis for this test is that the distributions of the locations of corresponding sample points are the same regardless of the groups. Hence, higher pvalues here would mean the group differences are significant and are not from the same distribution.”
I have a few questions regarding this 

When I previously used the Hotelling T^2 test, it was to compare mean shapes between groups to see if they were statistically significant. How does this calculate pvalues at every correspondence point? I would like to use this feature map but don’t feel comfortable yet with limited understanding of its mathematical mechanisms.

If the null hypothesis if that they are from the same distribution, wouldn’t lower pvalues indicate we are able to reject the null hypothesis and thus they are not from the same distribution?
Thanks for your help!