Diffusion weighted imaging (DWI) is known to be prone to artifacts related to motion originating from subject movement, cardiac pulsation and breathing, but also to mechanical issues such as table vibrations. Given the necessity for rigorous quality control and motion correction, users are often left to use simple heuristics to select correction schemes, but do not fully understand the consequences of such choices on the final analysis, moreover being at risk to introduce confounding factors in population studies. This paper reports work in progress towards a comprehensive evaluation framework of HARDI motion correction to support selection of optimal methods to correct for even subtle motion. We make use of human brain HARDI data from a well controlled motion experiment to simulate various degrees of motion corruption. Choices for correction include exclusion or registration of motion corrupted directions, with different choices of interpolation. The comparative evaluation is based on studying effects of motion correction on three different metrics commonly used when using DWI data, including similarity of fiber orientation distribution functions (fODFs), global brain connectivity via Graph Diffusion Distance (GDD), and reproducibility of prominent and anatomically defined fiber tracts. Effects of various settings are systematically explored and illustrated, leading to the somewhat surprising conclusion that a best choice is the alignment and interpolation of all DWI directions, not only directions considered as corrupted.