overquantification

Overquantification refers to the excessive reliance on quantitative measurement to describe, judge, or manage social, scientific, or organizational phenomena, often beyond what metrics can validly capture. It treats numbers as primary evidence and can overlook qualitative aspects, context, and uncertainty.

In policy, administration, business, science, and education, metrics such as performance indicators, dashboards, p-values, h-indices, standardized test scores, and budget-linked KPIs can shape decision making. When metrics become targets or data collection favors what is easy to measure over what matters, overquantification can distort goals and behavior.

Consequences include distorted priorities, gaming of metrics, neglect of unmeasured dimensions, privacy concerns, and wasted resources. It can also reduce complex phenomena to simplistic numbers, eroding credibility and trust.

Causes include incentive structures that reward metrics over outcomes, funding and publication pressures, data availability, and the belief that numbers enable objective comparison. Risks involve data quality problems, overfitting, p-hacking, and misinterpretation of correlation as causation.

Mitigation emphasizes validity, reliability, and context. Approaches include mixed methods, triangulation, theory-driven metrics, and careful review of metric relevance, transparency about limitations, and ethical governance. Used judiciously, quantification supports decision making; overquantification occurs when metrics drive decisions at the expense of meaning and quality.