statcentric

Statcentric is an adjective used to describe an approach in which statistics are treated as the central organizing principle in study design, data analysis, and interpretation. The term blends "statistic" with the suffix "-centric" to indicate emphasis on quantitative measurement as the primary driver of conclusions. While not universally adopted or standardized, statcentric discourse appears in discussions of methodology, data science, and evidence-based practice to contrast with more theory- or domain-centric approaches.

In practice, a statcentric approach foregrounds statistical considerations early in the research workflow. This includes explicit specification of endpoints, power analyses, sampling plans, measurement validity, and robustness checks before data collection. It also favors transparent reporting of effect sizes, confidence intervals, and sensitivity analyses, and often encourages preregistration or registered reports to curb analytic flexibility. Bayesian methods, frequentist proper design, and robust statistics can all be part of a statcentric framework, united by a shared aim of quantitative credibility.

Applications span experimental sciences, clinical trials, UX testing, economics, and policy analysis, where decision-making depends on reliable quantitative evidence. Critics argue that a strict statcentric stance can downplay theory, mechanism, or contextual factors, risking overemphasis on p-values or superficial metrics. Proponents, however, contend that a disciplined statistical core improves reproducibility and comparability across studies. See also: statistics, experimental design, preregistration, evidence-based practice, data science.