In grad school scale-free networks were the soup du jour and my advisor was hammering on me to show that the human metabolic networks I studied had that propert

In grad school scale-free networks were the soup du jour and my advisor was hammering on me to show that the human metabolic networks I studied had that property. "Guaranteed paper in Nature, get on it!", he exhorted. I sensed folks were not using the right statistical tests to show that distributions were scale-free (hint, doing a log-log-plot and doing linear regression to get a slope does not give you the base of the power law if there is no power law in the first place) and found a few notes on homepages that this was the case, but no publications (this was 2006 or so). There seems to be an attitude in the physical sciences that math is there as sort of a glaze or condiment you can throw on top of bad data to make it palatable. I didn't make many friends in the biology department by telling them their pet model they'd based their careers off of wasn't only bolstered by $trendy_math_analysis but actually weakened by it. People seemed less interested in truth and more interested in appearance and publication prestige.