Statistical discrimination is a concept that occurs when individuals or groups are treated differently based on the average characteristics of the group they belong to rather than their individual qualities. This practice often arises from using stereotypes or generalizations about a particular group, which can lead to unfair or biased decision-making.
For example, imagine you’re a manager at a tech company looking to hire a new employee. You have two candidates: one is a young, recent graduate, and the other is an older, experienced professional. You might assume that the younger candidate is more tech-savvy and up-to-date on the latest trends, while the older candidate might be less adaptable to change. If you make your hiring decision based on these assumptions rather than each candidate’s actual skills and experience, you’re engaging in statistical discrimination.
Although it’s not always intentional, statistical discrimination can have significant negative consequences, perpetuating stereotypes and reinforcing social inequalities. It’s essential to treat individuals as unique, considering their personal attributes, skills, and experiences, rather than relying on assumptions about the group they belong to.