verdict concept

Definition	The outcome of a registered test, pass, fail, or abstain, taken as the worst group with the groups too thin to score counted. Chapter 1 section 1.5 and chapter 10 section 10.4.
Example	Strata at 0.95, 0.93, 0.97, and 0.66 against a bar of 0.90 give the verdict fail, with the failing stratum named.
Book	Data Mining as Observation, draft 0.2, commit `f3914f0`; entry id `verdict`, kind concept.
Status	no ledger row names this entry. Corrections: none recorded.
Defining equation	Book equation 10.7.
Assumptions and scope	The outcome of a registered test, pass, fail, or abstain, taken as the worst group with the groups too thin to score counted. A verdict at one bar implies the verdict at every lower bar, and an abstention is reported as a verdict and not dropped. A verdict is relative to a budget and does not transfer to another, and the same two codes get opposite verdicts from two output metrics, so a verdict names its bar, its null, its budget, and its consumer.
Prior art	none recorded
Evidence	`lean/DataMiningAsObservation/Bar.lean`, `lean/DataMiningAsObservation/Abstention.lean`
Reviewed	not yet reviewed; generated 2026-09-10 from records at the commits on the provenance page.

Pass, fail, or abstain, taken as the worst group.

Equation

Book equation 10.7.

\[\text{verdict}=\min_{i:\ n_i\ge n_{\min},\ |Q_i|\ge q_{\min}}\ \mathrm{score}_i,\qquad \text{ABSTAIN otherwise}.\]

Book equation 14.5.

\[\mathrm{FC}_g=\Pr\big[y=\text{violation}\ \big|\ \hat y=\text{clear},\ g\big],\qquad \text{verdict}=\max_{g:\ n_g\ge n_{\min}}\mathrm{FC}_g,\qquad \text{ABSTAIN otherwise}.\]

Conditions

The outcome of a registered test, pass, fail, or abstain, taken as the worst group with the groups too thin to score counted. A verdict at one bar implies the verdict at every lower bar, and an abstention is reported as a verdict and not dropped.
A verdict is relative to a budget and does not transfer to another, and the same two codes get opposite verdicts from two output metrics, so a verdict names its bar, its null, its budget, and its consumer.

Conditions are curated in entries.toml rather than read from a record.

Ledger

none

First stated

Chapter 1 section 1.5 and chapter 10 section 10.4 of Data Mining as Observation, with the six classes in geometric-observation/PROTOCOL.md:58-75.

Measurements

Where the book states it	Numbers, as the book’s sources table records them	Source
chapter 9 section 9.2	12 of 12, dimensions 1.81, 2.80, 1.74, angular Spearman ranges, eccentricity spreads, verdict confirmed, GO-P-2026-041	`the-angular-observer/experiments/manifold-recovery/battery_result.json`

Failures and corrections

none

Invariance envelope

none declared

Machine checked

lean/DataMiningAsObservation/Bar.lean, theorems passes_anti, passes_mono, discriminates_iff, no_bar_of_null_ge, exists_bar_of_lt, vacuous_of_null_passes, at observation-data-mining f3914f0; what the check covers is stated in the book’s appendix C.

lean/DataMiningAsObservation/Abstention.lean, theorems abstain_not_passes, verdict_eq_none_iff, passes_verdict_iff, inf_le_of_subset, at observation-data-mining f3914f0; what the check covers is stated in the book’s appendix C.

Used in

Data Mining as Observation chapters 0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14.

bar; abstention; ledger class; min-over-strata; certificate.

Status

Generated 2026-09-10 by encyclopedia/generate.py; book at observation-data-mining f3914f0; the commit of every record is listed in the encyclopedia’s provenance.

← vectorwater-filling →