Experimental Designs¶
Cochran, W. G., & Cox, G. M. (1957). Experimental Designs. John Wiley & Sons.
Cited by¶
5 citations across 5 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Blocking (In Experimental Design)
- 2. The variance-reduction via known-source separation — nuisance variance attributable to the blocking dimension is removed from the residual error term in analysis and absorbed into the block effect, thereby reducing unexplained noise and improving precision of treatment-effect estimates and statistical power
This sourceCanonical exposition of randomized-block and factorial designs showing how nuisance variance attributable to the blocking dimension is absorbed into the block effect and removed from residual error
- 2. The variance-reduction via known-source separation — nuisance variance attributable to the blocking dimension is removed from the residual error term in analysis and absorbed into the block effect, thereby reducing unexplained noise and improving precision of treatment-effect estimates and statistical power
- Factorial Design
- T5 — Main-effect interpretation versus interaction complexity. Factorial designs often reveal substantial interactions that contradict simple main-effect stories — e.g., "high temperature is always better" becomes "high temperature is better with catalyst A but worse with catalyst B"
This sourcecomprehensive catalog of randomized-block and factorial designs and their analysis, treating interactions that contradict simple main-effect stories (e.g., factor effects reversing across levels of another factor).
- T5 — Main-effect interpretation versus interaction complexity. Factorial designs often reveal substantial interactions that contradict simple main-effect stories — e.g., "high temperature is always better" becomes "high temperature is better with catalyst A but worse with catalyst B"
- Randomization
- Once decomposed, each component has domain-specific best practice (allocation concealment, blinding, treatment fidelity, outcome validity, intention-to-treat analysis)
This sourceCochran Cox Experimental Designs randomized-block factorial variance-reduction.
- Once decomposed, each component has domain-specific best practice (allocation concealment, blinding, treatment fidelity, outcome validity, intention-to-treat analysis)
- Variability
- A randomized clinical trial comparing blood pressure responses to two drugs illustrates the core variability framework
This sourceCochran Cox Experimental Designs randomized-block factorial variance-reduction.
- A randomized clinical trial comparing blood pressure responses to two drugs illustrates the core variability framework
Mechanisms¶
- Randomized Complete-Block Design
- It also assumes treatment effects are roughly additive across blocks; a strong block-by-treatment interaction means the single averaged effect hides real heterogeneity.
This sourceModels randomized blocks additively and treats block-by-treatment variation as evidence that treatment effects differ across blocks rather than as one homogeneous effect.
- It also assumes treatment effects are roughly additive across blocks; a strong block-by-treatment interaction means the single averaged effect hides real heterogeneity.
Verification¶
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Links previously used in the corpus¶
Before the registry existed this work was also linked 2 other ways.
- https://search.worldcat.org/title/Experimental-designs/oclc/1017107728 ×1
- https://www.wiley.com/en-us/Experimental+Designs,+2nd+Edition-p-9780471545675 ×1
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