We demonstrate a meaningful prospective power analysis for an (admittedly
idealized) illustrative connectome inference task. Modeling neurons as vertices
and synapses as edges in a simple random graph model, we optimize the trade-off
between the number of (putative) edges identified and the accuracy of the edge
identification procedure. We conclude that explicit analysis of the
quantity/quality trade-off is imperative for optimal neuroscientific
experimental design. In particular, more though more errorful edge
identification can yield superior inferential performance.