Antecedent Probability and the Efficiency of Psychometric Signs, Patterns, or Cutting Scores¶
Meehl, P. E., & Rosen, A. (1955). Antecedent Probability and the Efficiency of Psychometric Signs, Patterns, or Cutting Scores. Psychological Bulletin, 52(3), 194-216.
Cited by¶
5 citations across 5 artifacts.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Anomaly Detection Model
- Border Checkpoint
- False positives strand or detain travelers who should cross; profiling substitutes group proxies for real risk; and base-rate neglect
This sourceShows that when the target condition is rare, a positive result from a fallible screen can consist mostly of false positives, especially under aggressive cutoffs.
- False positives strand or detain travelers who should cross; profiling substitutes group proxies for real risk; and base-rate neglect
- Exception Flag Rules Engine
- When the base rate of real deviations is low, even an accurate rule produces mostly false positives — the base rate fallacy — and the flag stream buries the rare true exception in noise.
This sourceDemonstrates that low base rates can make a seemingly valid rule's positive calls predominantly false.
- When the base rate of real deviations is low, even an accurate rule produces mostly false positives — the base rate fallacy — and the flag stream buries the rare true exception in noise.
- Red-Flag Screen
- For a rare problem
This sourceShows how a low base rate can make most positive classifications false positives even when the screening sign has useful accuracy.
- For a rare problem
- Risk Score Cutoff
- … failure is the base-rate trap: when the true risk is rare, even an accurate score produces mostly false positives above any useful cutoff, because the vast low-risk population dwarfs the few real cases — a fixed line then floods reviewers with wrongly flagged cases and quietly harms the people just above it.
This sourceShows that low base rates can cause a cutting score to produce many false-positive classifications because the negative population is much larger than the true-case population.
- … failure is the base-rate trap: when the true risk is rare, even an accurate score produces mostly false positives above any useful cutoff, because the vast low-risk population dwarfs the few real cases — a fixed line then floods reviewers with wrongly flagged cases and quietly harms the people just above it.
Verification¶
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