Calibrating Model-Based Inferences and Decisions

TL;DR

This paper reviews how model-based statistical inference methods, both frequentist and Bayesian, help quantify uncertainty and calibrate decision-making in complex experiments, emphasizing the importance of scrutinizing modeling assumptions.

Contribution

It provides a comprehensive review of procedures for calibrating inferences and decisions within frequentist and Bayesian frameworks for complex experimental data.

Findings

Model-based methods naturally define procedures for quantifying inferential uncertainty.

Calibration of decision-making processes is essential to avoid misleading conclusions.

The paper discusses implementation strategies for these procedures.

Abstract

As the frontiers of applied statistics progress through increasingly complex experiments we must exploit increasingly sophisticated inferential models to analyze the observations we make. In order to avoid misleading or outright erroneous inferences we then have to be increasingly diligent in scrutinizing the consequences of those modeling assumptions. Fortunately model-based methods of statistical inference naturally define procedures for quantifying the scope of inferential outcomes and calibrating corresponding decision making processes. In this paper I review the construction and implementation of the particular procedures that arise within frequentist and Bayesian methodologies.