AbstractHow do people make evaluations when important items of information are missing? In the context of personnel evaluations or product evaluations, researchers have proposed that decision makers may predict the missing attribute based on its assumed relationship to attributes that are present. In addition, some researchers have suggested that there is a penalty for missing information. An experiment was conducted to investigate the influence of the importance of the missing attribute on the hypothesized inference effect. Hypothetical job candidates were evaluated based on one or two attributes. The correlation between the attributes was varied between groups of subjects as was the importance of one of the attributes. The pattern of ratings of candidates with missing information varied significantly with the correlation condition when the missing attribute was very important, but did not vary as much when the missing attribute was less important. The results were generally consistent with the predictions of the Inferred Information Model (Johnson and Levin, 1985). On average, candidates with missing information were rated lower than comparable candidates with complete information and the missing attribute at an average level.