Robust Inference Policies

TL;DR

This paper evaluates the robustness of various inference procedures under judgmental imprecision, revealing that Bayesian methods are more powerful yet more prone to errors compared to linear models.

Contribution

It provides a comparative analysis of inference procedures, highlighting the trade-offs between power and error susceptibility in Bayesian versus linear models.

Findings

Bayesian procedures more likely to support true hypotheses

Bayesian procedures also more likely to support false hypotheses

Bayesian methods are more powerful but error-prone

Abstract

A series of monte carlo studies were performed to assess the extent to which different inference procedures robustly output reasonable belief values in the context of increasing levels of judgmental imprecision. It was found that, when compared to an equal-weights linear model, the Bayesian procedures are more likely to deduce strong support for a hypothesis. But, the Bayesian procedures are also more likely to strongly support the wrong hypothesis. Bayesian techniques are more powerful, but are also more error prone.