The Magnitude Heuristic: Larger Differences Increase Perceived Causality

With the rise of machine learning and “big data,” large yet spurious associations are increasingly likely to be discovered, leveraged in marketing communications, and publicized in the media. Thus, consumers are increasingly exposed to many associations with large magnitudes that do not signal causal relationships. We find that consumers’ unprecedented exposure to the many associations with large magnitudes contained in big data carries a substantial cost: this magnitude information can distort consumers’ judgments about whether or not a correlation reflects a causal relationship. In particular, consumers often use a magnitude heuristic: they infer that associations with larger perceived magnitudes are more likely to reflect causality, even when they are not. In many situations, relying on such a heuristic biases causality judgments, such as when large associations are spurious or when extraneous factors (e.g., reference points) distort perceptions of magnitudes. Across six studies, we document people’s reliance on the magnitude heuristic and illuminate how it underlies many of the causality judgments that consumers make daily. Because consumers often purchase products that they believe will cause goal-consistent outcomes, we further find that magnitude-distorted (mis)perceptions of causality can distort consumers’ product decisions. Implications for both consumers and marketers are discussed.