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.
Randomized tests
Definition
A randomized test is a randomized procedure with binary action space whose action set is , equipped with its full sigma-algebra (the power set, which is a sigma-algebra by Sigma-algebras), where means reject and means do not reject (Randomized procedures as probability kernels). Its test function is Since is a probability measure and partition the action set, . Thus and is the probability of not rejecting. Give the trace of the Borel sigma-algebra on , namely . By A randomized procedure has a measurable action law, is measurable; since it has range in , its preimage of equals its preimage of and is measurable for every such . Hence is a measurable map in the preimage sense of A measurable function between measurable spaces. Its value is the rejection probability after observing . The convention is fixed here. No null or alternative hypothesis is part of this definition.
Depends on
Used by
- The one-sample Hotelling F test has exact size alpha Corollary
- A nonmeasurable rule is not a statistic or test Counterexample
- Finite power-oracle queries cannot universally certify no feasible sample size Counterexample
- Anytime-valid p-value processes Definition
- Critical, detection, and quantification levels Definition
- Finite type-II testing function with explicit orientation Definition
- Likelihood-ratio tests with boundary randomization Definition
- Power and sample-size planning specifications Definition
- Test size and power Definition
- A binary Neyman–Pearson boundary randomization Example
- A coin-flip randomized test Example
- A randomized UMP test for a binomial model Example
- Opposed most powerful tests for two normal alternatives Example
- Binary zero-one loss risks are the testing error probabilities Lemma
- Every measurable rejection function defines a randomized test Lemma
- Both tails determine power for the known-variance two-sided normal z-test Proposition
- A two-sided normal alternative need not have a UMP test Theorem
- Exact finite-group permutation p-values and tests Theorem
- Exact limits in the known-blank equal-variance normal model Theorem
- Exact power and least sample size for a known-variance normal z-test Theorem
- Exact t and partial F inference in full-rank Gaussian regression Theorem
- Neyman–Pearson lemma with singular alternative mass Theorem
- The Karlin–Rubin theorem for one-sided tests Theorem
- Valid p-values yield level tests for nonempty disjoint alternatives Theorem
- Validity of identity-corrected random-permutation Monte Carlo p-values Theorem
Dependency tree · two levels
22 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Ryan Martin, Lecture Notes on Advanced Statistical Theory, §5.1, PDF pp. 100–102 (standard reference, not scraped)
- Peter Hoff, Statistical Decision Problems, §3, PDF pp. 4–5 (standard reference, not scraped)