How Do Auditors Address Control Deficiencies that Bias Accounting Estimates?
Bibliographic record
Abstract
Auditors commonly rely on reviewing management's estimation process to audit accounting estimates. When control deficiencies bias the estimation process by creating omissions of critical inputs, standards require that auditors replace or supplement review of management's estimation process with tests that can identify the omissions. Importantly, overreliance on reviewing management's estimation process when it has been biased by a control deficiency can result in auditor acceptance of an inappropriate accounting estimate. We use an experiment to examine whether auditors recognize the insufficiency of increased sampling of a biased estimation process and their selection of alternative tests to replace or supplement review of the biased estimation process. We find that a significant minority (33 percent) of Big 4 senior auditors erroneously increase tests of management's biased estimation process. We also find that auditors have difficulty selecting alternative tests to replace or supplement review of management's biased estimation process, frequently choosing tests that are either ineffective or inefficient. Our findings suggest that auditors often reach inappropriate judgments about the capability of audit evidence to address control deficiencies and that nonsampling risk (judgment risk) may be a larger risk than auditors realize.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.153 | 0.596 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".