A Note on the Interdependence between Hypothesis Generation and Information Search in Conducting Analytical Procedures*
Bibliographic record
Abstract
Abstract This study examines the linkage among the initial hypothesis set, the information search, and decision performance in performing analytical procedures. We manipulated the quality of the initial hypothesis set and the quality of the information search to investigate the extent to which deficiencies (or benefits) in either process can be remedied (or negated) by the other phase. The hypothesis set manipulation entailed inheriting a correct hypothesis set, inheriting an incorrect hypothesis set, or generating a hypothesis set. The information search was manipulated by providing a balanced evidence set to auditors (i.e., evidence on a range of likely causes including the actual cause ‐ analogous to a standard audit program) or asking them to conduct their own search. One hundred and two auditors participated in the study. The results show that auditors who inherit a correct hypothesis set and receive balanced evidence performed better than those who inherit a correct hypothesis set and did their own search, as well as those who inherited an incorrect hypothesis set and were provided a balanced evidence set. The former performance difference arose because auditors who conducted their own search were found to do repeated testing of non‐errors and truncated their search. This suggests that having a correct hypothesis set does not ensure that a balanced testing strategy is employed, which, in turn, diminishes part of the presumed benefits of a correct hypothesis set. The latter performance difference was attributable to auditors' failure to generate new hypotheses when they received evidence about a hypothesis that was not in the current hypothesis set. This demonstrates that balanced evidence does not fully compensate for having an initial incorrect hypothesis set. These findings suggest the need for firm training and/or decision aids to facilitate both a balanced information search and an iterative hypothesis generation process.
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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 | 0.373 | 0.794 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".