“Investigators will have to focus on the scientific quality of the evidence, and lesson the statistic methods… recalling the old adage that statistics are like a bikini bathing suit: what is revealed is interesting; what is concealed is crucial.”

– Dr. Alvan Feinstein

Even archaeologists are using big data. Centurions and those who followed them seem to have broken pottery all over what is now becoming post-nationalist Europe. I spent a lot of time using big data in the ‘80s, and learned the hard way that no amount of data can make up for lack of experiential learning and wisdom (for a fictionalized account, see Connie’s Conception). I was spoiled, then. I had worked with archival data of the highest quality: vital records and satellite imaging that was precise and accurate. But I realize now that they were largely irrelevant, being more social and physical than biological.

A couple of decades ago, when the modern adoration of big data was boosted by security fears, I began to notice the extent to which people believed that quantity could overcome lack of quality. I also began to notice how bad some of the big data were. The most noticeable was the change at the Canadian border. I was held up every time—after having crossed for years without difficulty. Apparently there was a dope smuggler with my name, and my name had been flagged. It took a while for the penny to drop: no middle name and no picture in the database! What could go wrong? A decade on, my son and his friends in IT were selling data to advertisers; the data looked good—and the profit was substantial. Did they do any more than treat client anxiety by beefing up the illusion of control? I think not.

So why do I bring it up? The big British cellphone study. Like most policy-oriented studies of non-ionizing radiation, this one is big and simple—too simple to make biomedical sense. Studies tracking chemical exposures through armbands are likewise limited by crude assessment of a piece of an exposure with no attention to total end-organ exposure in or over time. The assumption that what isn’t measured doesn’t matter has never been so hazardous or obstructive. Small N-of-1 studies would enable patients to protect themselves. And, aggregated high-quality data could yield useful knowledge decades faster, and be far less misleading.