Checking election outcome accuracy Post-election audit sampling

TL;DR

This paper reviews and improves methods for calculating post-election audit sample sizes, introducing new formulas, bounds, and discussing common pitfalls to enhance election outcome verification accuracy.

Contribution

It presents novel margin error bounds, sampling weights, and simple formulas for audit sample size estimation applicable to various election contexts.

Findings

New margin error bounds and sampling weights improve audit accuracy.

Two simple formulas enable sample size estimation without detailed data.

Enhanced methods increase the effectiveness of risk-limiting post-election audits.

Abstract

This article * provides an overview of post-election audit sampling research and compares various approaches to calculating post-election audit sample sizes, focusing on risklimiting audits, * discusses fundamental concepts common to all risk-limiting post-election audits, presenting new margin error bounds, sampling weights and sampling probabilities that improve upon existing approaches and work for any size audit unit and for single or multi-winner election contests, * provides two new simple formulas for estimating post-election audit sample sizes in cases when detailed data, expertise, or tools are not available, * summarizes four improved methods for calculating risk-limiting election audit sample sizes, showing how to apply precise margin error bounds to improve the accuracy and efficacy of existing methods, and * discusses sampling mistakes that reduce post-election audit effectiveness.