This article analyzes statistical inference as a constitutive methodological framework rather than a purely descriptive or discovery-oriented procedure. It argues that statistical models, inferential conventions, and validation criteria actively structure the space of admissible patterns, parameter interpretations, and causal claims. Drawing on the foundations of statistical methodology and post-positivist philosophy of science, the study examines the historical formation of core inferential concepts and critically evaluates the role of modeling assumptions—such as independence, distributional form, and error structure—in shaping empirical conclusions. Particular attention is given to the performative effects of significance testing, model selection, and threshold-based decision rules in stabilizing empirical results and guiding scientific practice. The analysis is extended to contemporary data-intensive and algorithmic settings, where machine learning and large-scale statistical modeling further amplify these constitutive dynamics. The article concludes that statistical objectivity is inseparable from formal modeling choices and inferential conventions, highlighting the necessity of reflexive methodological scrutiny in statistical practice.
Caio A. Rocha (Wed,) studied this question.