notstatistical

Notstatistical is a term used in data science and philosophy of science to describe a stance, approach, or set of practices that deliberately minimize or forgo formal statistical methods in favor of non-quantitative evidence or qualitative reasoning. It does not imply a blanket rejection of statistics, but rather a contextual choice when statistical models are viewed as inappropriate, inaccessible, or insufficient to capture a problem’s complexity. The term has appeared in discussions about policy evaluation, journalism, and design research as a label for methods that emphasize context, mechanism, case-based analysis, and triangulation of multiple sources.

In practice, notstatistical work might rely on expert interviews, field observations, case studies, narrative synthesis, process tracing, and theory-driven explanation. It seeks to illuminate causal mechanisms and contextual factors rather than producing generalizable effect sizes or p-values. Notstatistical reasoning often aims for transparency about assumptions, data limitations, and the intended scope of inference, helping users understand where conclusions come from and why they matter.

Notstatistical approaches are frequently seen as complementary to statistical analysis. Proponents argue they improve relevance, explainability, and ethical accountability, while critics warn of subjectivity and limited generalizability. In balanced research, notstatistical reasoning can accompany statistical results to provide mechanism-based interpretations and policy relevance.

See also: qualitative research, mixed methods, mechanism-based explanation, evidence-based policy, transparency in data science.