The Neglect of Base Rate Data by Human Resources Managers in Employee Selection
Bibliographic record
Abstract
Abstract This study investigated the use of base rate information in decision making by individual and groups of human resources (HR) managers. A 2 times 2 (base rate x decision entity) mixed factorial design was used. Data were collected from 91 managers who were subsequently placed into 30 groups. The managers were presented with a written scenario describing a selection decision. The scenario contained both the prior probability that a job candidate had partnership potential and the candidate's score from a structured selection interview. Analysis of variance (ANOVA) confirmed that HR managers, both individually and collectively, were insensitive to the base rate data and relied too heavily on the interview scores. The HR managers as a result made inaccurate judgments about a job candidate's potential. Implications for the decision‐making and selection literatures are discussed. Résumé Cette étude a pour objet l'emploi de taux de probabilité de base (tablis sur un échantillonage préalable) dans les prises de décision par des gestionnaires en ressources humaines, pour des décisions effectuées tant sur un plan individuel que collectif. L'étude emploie un plan expérimental factoriel de 2 times 2 (probabilité de base x agent(s) de décision). Les données furent recueillies auprès de 91 gestionnaires, lesquels furent ensuite répartis en 30 groupes. Une situation fictive décrivant un choix d'embauche leur fut présenté. Le cas fictif indiquait la probabilité qu'avait un candidat de devenir éventuellement un associé d'une société, de même que le score du candidat dans une entrevue de sélection évaluative. L'analyse de la variance (Angl. ANOVA) confirme que les gestionnaires en ressources humaines, individuellement de même qu'en groupe, privilégiaient les résultats de l'entrevue aux dépens des probabilités de base. Par conséquent, les gestionnaires évaluaient assez imprécisément le potentiel du candidat. L'auteur discute ensuite les consequences de ces résultats pour les prises de décision en général, ainsi que pour l'étude des stratégies d'évaluation du personnel.
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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 | 0.082 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".