Detecting Outliers in Multiple Sampling Results Without Thresholds

TL;DR

This paper introduces a threshold-free method for detecting outliers in multiple sampling results, addressing challenges in Bayesian analysis and selection effects without relying on arbitrary thresholds.

Contribution

It proposes a novel approach to identify outliers without predefined thresholds, improving robustness in Bayesian analysis of multiple samples.

Findings

Effective outlier detection without thresholds demonstrated

Method reduces bias from selection effects

Applicable to various sampling scenarios

Abstract

Bayesian statistics emphasizes the importance of prior distributions, yet finding an appropriate one is practically challenging. When multiple sample results are taken regarding the frequency of the same event, these samples may be influenced by different selection effects. In the absence of suitable prior distributions to correct for these selection effects, it is necessary to exclude outlier sample results to avoid compromising the final result. However, defining outliers based on different thresholds may change the result, which makes the result less persuasive. This work proposes a definition of outliers without the need to set thresholds.