Beyond p-Values: A Comparative Framework for Estimation-Based Inference Using the A Priori Procedure and Gain–Probability Analysis

Tonghui Wang; S.T. Boris Choy; David Trafimow; Xiangfei Chen; Ziyuan Wang

doi:10.63065/ajafe.2026.2.001

Asian Journal of Applied Financial Econometrics (AJAFE)

Volume 1_Number 2 , Tháng 8/2026, Trang 5-20

Beyond p-Values: A Comparative Framework for Estimation-Based Inference Using the A Priori Procedure and Gain–Probability Analysis

Tonghui Wang, S.T. Boris Choy, David Trafimow, Xiangfei Chen, Ziyuan Wang

DOI: 10.63065/ajafe.2026.2.001

Ngày nhận bài: 17/09/2026

Ngày nhận bài sửa: 30/08/2026

Ngày duyệt đăng: 09/09/2026

Email: twang@nmsu.edu

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Tóm tắt

This paper compares three inferential paradigms: Null Hypothesis Significance Testing (NHST), the A Priori Procedure (APP), and Gain Probability (G–P) analysis, with the latter two paradigms featuring a conceptual shift away from binary hypothesis testing toward inference frameworks grounded in estimation and probability. NHST relies on p-values and dichotomous decision rules; in contrast, APP emphasizes pre-data estimation reliability, and G–P analysis quantifies the probability that one outcome exceeds another, and by varying extents. This shift aligns with broader methodological reforms prioritizing estimation, transparency, and interpretability. In addition to their conceptual advantages, the APP and G-P paradigms are highly applicable. To advance this perspective, we examine the derivation of APP for parameter estimation in linear regression models under skew-normal error structures, including results on the distribution of regression estimators, supported by simulation studies and empirical illustrations. We also investigate the G–P framework for gamma-distributed variables in both independent and dependent settings, again reinforced by simulation studies and real-world applications. Collectively, these frameworks provide a rigorous and interpretable alternative to conventional significance testing by prioritizing estimation accuracy, probabilistic interpretation, and practical relevance.

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