摘要 : With the rise of machine learning and "big data," many large yet spurious relationships between variables are discovered, leveraged by marketing communications, and publicized in the media. Thus, consumers are increasingly exposed... 展开 With the rise of machine learning and "big data," many large yet spurious relationships between variables are discovered, leveraged by marketing communications, and publicized in the media. Thus, consumers are increasingly exposed to many large-magnitude relationships between variables that do not signal causal effects. This exposure may carry a substantial cost. Seven studies demonstrate that the magnitudes of relationships between variables can distort consumers' judgments about whether those relationships reflect causal effects. Specifically, consumers often use a magnitude heuristic: consumers infer that relationships with larger perceived magnitudes are more likely to reflect causal effects, even when this is not true (and even when relationships' correlations are held constant). In many situations, relying on the magnitude heuristic will distort causality judgments, such as when large-magnitude relationships between variables are spurious, or when nor-matively extraneous factors (e.g., reference points) distort perceptions of magnitudes. Moreover, magnitude-distorted (mis)perceptions of causality, in turn, distort consumers' purchase and consumption decisions. Since consumers often encounter spurious relationships with large magnitudes in the health domain and in other consequential domains, the magnitude heuristic is likely to lead to biases in some of consumers' most important decisions. 收起