Seeing Stars: How the Binary Bias Distorts the Interpretation of Customer Ratings

Abstract Across many different contexts, individuals consult customer ratings to inform their purchase decisions. The present studies document a novel phenomenon, dubbed “the binary bias,” which plays an important role in how individuals evaluate customer reviews. Our main proposal is that people tend to make a categorical distinction between positive ratings (e.g., 4s and 5s) and negative ratings (e.g., 1s and 2s). However, within those bins, people do not sufficiently distinguish between more extreme values (5s and 1s) and less extreme values (4s and 2s). As a result, people’s subjective representations of distributions are heavily impacted by the extent to which those distributions are imbalanced (having more 4s and 5s vs. more 1s and 2s). Ten studies demonstrate that this effect has important consequences for people’s product evaluations and purchase decisions. Additionally, we show this effect is not driven by the salience of particular bars, unrealistic distributions, certain statistical properties of a distribution, or diminishing subjective utility. Furthermore, we demonstrate this phenomenon’s relevance to other domains besides product reviews, and discuss the implications for existing research on how people integrate conflicting evidence.