LEVERAGING STATISTICAL SAMPLING TECHNIQUES IN FINANCIAL AUDITING TO IMPROVE COMPLIANCE, ACCURACY, AND TRUST IN REPORTED FINANCIAL STATEMENTS

This study investigates the impact of leveraging statistical sampling techniques in financial auditing to enhance compliance, accuracy, and stakeholder trust. The research employs a mixed-methods approach, integrating structured interviews with auditors and survey data from accounting firms across multiple industries. Quantitative analysis includes a chi-square test for compliance evaluation (χ² = 24.7, p < 0.05), regression analysis on audit accuracy (R² = 0.85, β = 0.74, p < 0.01), and a t-test for stakeholder trust levels (t = 5.68, p < 0.001), demonstrating a significant improvement in financial reporting quality. Findings reveal that the adoption of statistical sampling increased from 45% in 2020 to 72% in 2024, while audit accuracy improved from 80% to 90% over the same period. Compliance rates rose from 70% to 85%, and stakeholder trust in audited statements increased from 65% to 80%. The overall Pearson correlation coefficient (r = 0.89, p < 0.001) indicates a strong positive relationship between statistical sampling and financial audit effectiveness. The study concludes that statistical sampling enhances financial statement reliability but requires continuous adaptation to regulatory changes and technological advancements. Recommendations include integrating AI-driven sampling tools, enhancing auditor training, improving transparency, and implementing standardized compliance frameworks.