How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Coverage level is not the posterior probability of the realized confidence set
Statement refuted
Statement refuted: "if is a confidence set of level for a family and is any probability distribution on for which the conditional law of given the datum exists, then the conditional probability that given equals " (Confidence sets and their coverage level).
Facts & Assumptions
Given: The parameter set , the data space with the full power-set sigma-algebra, and, for each , the four-point set .
For each , is a probability measure on , where is the full power set, a sigma-algebra. (Measures on sigma-algebras, Probability measures and probability spaces, Sigma-algebras)
A confidence set is a map with each measurable, its coverage at is , and its level is when the infimum of the coverages is at least . (Confidence sets and their coverage level)
On a finite probability space with , the conditional probability of given is ; a finite weighted set determines such a probability measure and finite weighted sums give its probabilities. (Conditional probability for , Finite probability spaces, outcome weights, events, and event probabilities, Finite probability spaces are exactly finite full-power-set probability spaces)
Counterexample
By [L1] each determines a probability measure on with for and otherwise. Define Each is a subset of , and each set is a subset of and therefore an element of the full power set , so is a confidence set by [L2].
For fixed the four points of evaluate under as follows: gives , which contains ; gives , which is empty for and equals for ; in every case it does not contain ; and the two points with distinct coordinates, and , both give . Hence for every , and the coverage level of is by [L2].
Let . Since has distinct coordinates, step 1.1 gives . Moreover, holds exactly for , so equals for and for .
Fix weights summing to with , and put , a twelve-element set, and give weight . These nonnegative weights sum to , so [L3] defines a probability measure on the finite space . In events below, and denote the two coordinate maps on . The event has -probability , so the conditional probability given is defined by [L3].
The event equals , because by step 2.2; its -probability is , the same as by step 2.2. Hence
The confidence set has level by step 2.1, while for the prior and the datum the conditional probability that is by step 4.1; since , the refuted statement is false.
Remarks
The parameter is treated as random only through the explicitly specified weights ; the computation uses the finite weighted model and the conditional-probability identity of [L3]. The witness shows that the coverage level carries no data-conditional posterior content: the data can be consistent with exactly one parameter value while the level remains .
Depends on
Confidence sets and their coverage level
Conditional probability for
Finite probability spaces, outcome weights, events, and event probabilities
Measures on sigma-algebras
Probability measures and probability spaces
Sigma-algebras
Finite probability spaces are exactly finite full-power-set probability spaces
Dependency tree · 2 levels
Sources
- L. Wasserman, All of Statistics: A Concise Course in Statistical Inference (Springer, 2004), Example 6.14 (after Berger and Wolpert, 1984) and the warning in Section 6.3.2; full text copy at the cited URL
- S. Greenland, S. J. Senn, K. J. Rothman, J. B. Carlin, C. Poole, S. N. Goodman and D. G. Altman, 'Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations', European Journal of Epidemiology 31 (2016), 337–350