When it comes to understanding what makes people tick — and get sick — medical science has long assumed that the bigger the sample of human subjects, the better. But new research led by the University of California, Berkeley, suggests this big-data approach may be wildly off the mark.
That’s largely because emotions, behavior and physiology vary markedly from one person to the next and one moment to the next. So averaging out data collected from a large group of human subjects at a given instant offers only a snapshot, and a fuzzy one at that, researchers said. The findings, published this week in the Proceedings of the National Academy of Sciences journal, have implications for everything from mining social media data to customizing health therapies, and could change the way researchers and clinicians analyze, diagnose and treat mental and physical disorders.