Statistical Sampling Standards Enhancing Audit Reliability and Evidence Quality in Financial Reporting

Abstract

We examine how statistical sampling standards shape audit reliability and evidence quality in complex financial reporting environments. We analyze a global sample of 43 large scale audit engagements drawn from major audit markets and estimate the Audit Sample Integrity Model linking sample design methods, sample size determination, and error estimation procedures to evidence sufficiency, error detection capability, reporting accuracy, and assurance credibility under varying audit environment complexity. We find that structured sample design strengthens evidence sufficiency, quantitative and adaptive sample sizing improves error detection, and rigorous error estimation enhances reporting accuracy and assurance credibility. These effects are not uniform. Audit environment complexity conditions their strength, amplifying gains where methodological rigor scales with transaction volume, system diversity, and regulatory layering, and weakening outcomes where sampling decisions remain static. The core contribution lies in showing that statistical sampling operates as an integrated assurance architecture rather than a procedural compliance tool. By positioning environmental complexity as an active conditioning force, we extend audit quality theory and provide globally relevant guidance for regulators and audit firms seeking to strengthen evidence reliability in high risk engagements.