When Is the Averaging Effect Present in Auditor Judgments?
Bibliographic record
Abstract
ABSTRACT Auditing standards task auditors with collecting sufficient appropriate evidence to form audit judgments. Yet, cognitive psychology documents a robust finding in which people evaluate a bundle of relevant, directionally consistent evidence as though averaging the strength of the components. In consequence, a bundle of evidence may be viewed as weaker evidence than the bundle's strongest evidence item alone. We experimentally examine whether this averaging effect occurs in an audit context, and we test a potential moderator. In three independent mini‐cases, we ask auditor participants to make judgments about going concern, internal controls, and fraud risk. We present auditors with unfavorable audit evidence relevant to each judgment, manipulating whether we present a single strong evidence item or bundle it with a weaker evidence item. We also manipulate the auditor's initial impression of the client's state. We find that experienced auditors succumb to the averaging effect, making more strongly unfavorable judgments in response to the single evidence item than the bundle, and that this bias is reduced when the observed evidence is inconsistent with the auditor's initial impression. We interpret our results as consistent with the dual‐processing theory of cognition.
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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 | 0.013 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".