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 complete-block design
Definition
Fix integers and . There are blocks, each containing the same fixed experimental units, indexed within block by . The treatments form a fixed set with . A complete block is a block in which each treatment is assigned to exactly one unit. The blocks and their units are fixed before randomization.
Let be the set of permutations from the unit labels to . The randomized complete-block design (RCBD) uses assignment space and the uniform probability law on this finite nonempty set. Thus each has probability ; the within-block treatment permutations are independent and individually uniform. This is a finite randomized-treatment assignment model (Finite randomized treatment-assignment models) whose units are the cells and whose assignment is the only randomness supplied by the design. The design specifies no response model, Gaussian errors, additivity, or absence of interference. A pair is the special case .
Depends on
Used by
- Randomized Latin-square design Definition
- A Latin-square assignment is a strict subset of RCBD assignments Example
- A missing treatment falls outside the complete-block assignment space Example
- A paired contrast with unequal unit-level effects Example
- Moments of the effect for the randomly selected treated unit Lemma
- Nested contrast models and projections in a balanced block design Lemma
- One cell observation confounds unrestricted interaction with residual error Proposition
- Design expectation and variance of a paired contrast Theorem
- Exact Gaussian F law for RCBD treatment contrasts Theorem
Dependency tree · two levels
15 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
- Gary W. Oehlert, A First Course in Design and Analysis of Experiments, Chapter 13 §§13.1–13.2 (standard reference, not scraped)
- Susan Holmes and Wolfgang Huber, Modern Statistics for Modern Biology, Chapter 13 §§13.2 and 13.4.4 (standard reference, not scraped)