What does statistical discrimination refer to?

Statistical discrimination refers to the practice of applying group averages or characteristics to make decisions about individuals from that group. This concept arises from the notion that decision-makers may rely on statistical information about groups, assuming that the average traits or behaviors of a group apply to all its members. This can often result in unfair treatment of individuals based on generalized data rather than their unique attributes or qualifications.

For example, in a hiring process, an employer might assume that because a certain demographic performs below average in a particular skill as per statistical data, individuals from that demographic also lack that skill, ignoring their actual capabilities. This approach can lead to perpetuating stereotypes and systemic inequalities.

In contrast, the other options involve different forms of decision-making. Using individual characteristics emphasizes the importance of person-specific traits, which contradicts the generalized assumption inherent in statistical discrimination. Ignoring group data directly opposes the very essence of statistical discrimination, as it would involve not considering available data about groups. Relying solely on personal experience creates subjective biases that may not align with statistical trends or group characteristics, thereby lacking the systematic approach inherent in statistical discrimination.