I, Robot

When we evaluate and compare a range of data points - whether that data is related to health outcomes, head counts, or menu prices - we tend to neglect the relative strength of the evidence and treat it as simply binary, according to research published in Psychological Science, a journal of the Association for Psychological Science.

"People show a strong tendency to dichotomize data distributions and ignore differences in the degree to which instances differ from an explicit or inferred midpoint," says psychological scientist Matthew Fisher of Carnegie Mellon University.

In a series of six studies, Fisher and coauthor Frank C. Keil of Yale University examined how people tend to reduce a continuous range of data points into just two categories.

Binary bias distorts how we integrate information