Achieving More With Less: Intuitive Correction in Selection

Abstract

Choosing between candidates for a position can be tricky, especially when the selection test is affected by irrelevant characteristics (e.g., reading speed). One can correct for this irrelevant attribute by penalizing individuals who have unjustifiably benefited from it. Statistical models do so by including the irrelevant attribute as a suppressor variable, but can people do the same without the help of a model? In three experiments (total N = 357), participants had to choose between two candidates, one of whom had higher levels of an irrelevant attribute and thus enjoyed an unfair advantage. Participants showed a substantial preference for the candidate with high levels of the irrelevant attribute, thus choosing the less suitable candidate. This bias was attenuated when the irrelevant attribute was a situational factor, probably by making the correction process more intuitive. Understanding the intuitive judgment of suppressor variables can help candidates from underprivileged groups boost their chances to succeed.

Keywords

decision making judgment bias intuition suppressor open data open materials

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