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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 C is a confidence set of level 1−α for a family (Pθ)θ∈Θ and π is any probability distribution on Θ for which the conditional law of θ given the datum s exists, then the conditional probability that θ∈C(s) given s equals 1−α" (Confidence sets and their coverage level).

Facts & Assumptions

Given: The parameter set Θ={14,16,18}, the data space S=R2 with the full power-set sigma-algebra, and, for each θ∈Θ, the four-point set Dθ:={(θ−1,θ−1),(θ−1,θ+1),(θ+1,θ−1),(θ+1,θ+1)}.

[L1]

For each θ∈Θ, A↦∣A∩Dθ∣/4 is a probability measure on (S,S), where S=2S is the full power set, a sigma-algebra. (Measures on sigma-algebras, Probability measures and probability spaces, Sigma-algebras)

[L2]

A confidence set is a map C:S→2Θ with each {θ∈C} measurable, its coverage at θ is Pθ(θ∈C), and its level is 1−α when the infimum of the coverages is at least 1−α. (Confidence sets and their coverage level)

[L3]

On a finite probability space with P(B)>0, the conditional probability of A given B is P(A∣B)=P(A∩B)/P(B); a finite weighted set (W,w) determines such a probability measure and finite weighted sums give its probabilities. (Conditional probability P(A∣B) for P(B)>0, Finite probability spaces, outcome weights, events, and event probabilities, Finite probability spaces are exactly finite full-power-set probability spaces)

Counterexample

1.1givenL1L2

By [L1] each θ∈Θ determines a probability measure Pθ on (S,S) with Pθ({s})=1/4 for s∈Dθ and Pθ({s})=0 otherwise. Define C(s):={{s1−1}∩Θ,s1=s2,{s1+s22}∩Θ,s1≠s2. Each C(s) is a subset of Θ, and each set {θ∈C} is a subset of S and therefore an element of the full power set S=2S, so C is a confidence set by [L2].

2.1step 1.1L1L2algebra

For fixed θ the four points of Dθ evaluate under C as follows: (θ+1,θ+1) gives C={(θ+1)−1}={θ}, which contains θ; (θ−1,θ−1) gives C={θ−2}∩Θ, which is empty for θ=14 and equals {θ−2} for θ=16,18; in every case it does not contain θ; and the two points with distinct coordinates, (θ+1,θ−1) and (θ−1,θ+1), both give C={θ}. Hence Pθ(θ∈C)=3/4 for every θ∈Θ, and the coverage level of C is 3/4 by [L2].

2.2step 1.1L1algebra

Let s∗:=(15,17). Since s∗ has distinct coordinates, step 1.1 gives C(s∗)={(15+17)/2}={16}. Moreover, s∗∈Dθ holds exactly for θ=16, so Pθ({s∗}) equals 1/4 for θ=16 and 0 for θ∈{14,18}.

3.1step 2.2givenL3

Fix weights π(14),π(16),π(18)≥0 summing to 1 with π(16)>0, and put W:={(θ,s):θ∈Θ, s∈Dθ}, a twelve-element set, and give (θ,s)∈W weight π(θ)/4. These nonnegative weights sum to 1, so [L3] defines a probability measure Q on the finite space W. In events below, θ and s denote the two coordinate maps on W. The event {s=s∗} has Q-probability π(16)⋅14>0, so the conditional probability given s∗ is defined by [L3].

4.1step 2.2step 3.1L3algebra

The event {θ∈C(s), s=s∗} equals {θ=16, s=s∗}, because C(s∗)={16} by step 2.2; its Q-probability is π(16)⋅14, the same as Q({s=s∗}) by step 2.2. Hence Q(θ∈C(s) ∣ s=s∗)=π(16)/4π(16)/4=1.

5.1step 2.1step 4.1algebra∎

The confidence set C has level 3/4 by step 2.1, while for the prior π and the datum s∗ the conditional probability that θ∈C(s) is 1 by step 4.1; since 1≠3/4, the refuted statement is false.

Remarks

The parameter is treated as random only through the explicitly specified weights π; the computation uses the finite weighted model and the conditional-probability identity of [L3]. The witness shows that the coverage level 3/4 carries no data-conditional posterior content: the data can be consistent with exactly one parameter value while the level remains 3/4.

Depends on

Dependency tree · 2 levels

Sources