The Design of Experiments

Fisher, R. A. (1935). The Design of Experiments.

Type

Book

Intellectual base

Review or monograph

Year

1935

Link

https://archive.org/details/in.ernet.dli.2015.502684

Cited from

mathematics, philosophy, statistics_experimental_design

Cited by

16 citations across 16 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Blocking (In Experimental Design)
  • 1. The homogeneous experimental-unit grouping — units are stratified ex-ante into blocks where members within each block are similar on known nuisance dimensions (soil fertility, patient age, baseline outcome, location, batch), such that within-block comparisons isolate treatment effects from these nuisance sources

    This sourceOliver and Boyd, Edinburgh. Foundational treatise establishing randomization as the 'reasoned basis for inference' and developing the three principles of randomization, replication, and blocking

• Ceteris Paribus
  • In controlled experiments it is the discipline of holding all factors fixed except the manipulated variable, with randomization as the device that approximately implements it across unmeasured factors.

    This sourceEstablishes the controlled experiment and randomization as the device that holds unmeasured factors equal in expectation across treatment arms, implementing "all else equal," with residual confounding quantified by the analysis.

• Control Sample
  • The agricultural design discipline of replicated, randomized, blocked trials with controls, developed for field experiments, ported directly into online experimentation, marketing experiments, and operations testing nearly a century later, the design templates surviving the move intact.

    This sourceFounding text of randomized, replicated, blocked agricultural field trials with controls — the design template later ported to industrial and online experimentation.

• Experimental Design
  • Experimental design is the principled architecture of an empirical investigation structured to support causal or comparative inference under resource and ethical constraints, as Fisher (1935) established in his foundational treatment of randomization, blocking, and factorial design.

    This sourcefoundational treatise establishing randomization as the "reasoned basis for inference" and developing the principles of randomization, replication, blocking, and factorial design that underpin modern experimental design.

• Factorial Design
  • 1. The multi-factor combinatorial enumeration — the design explicitly covers all (or balanced subsets of) combinations of factor levels so that each factor is studied not in isolation but across multiple contexts (levels of other factors), enabling detection of context-dependent effects

    This sourcefoundational treatise developing factorial design alongside randomization, replication, and blocking; establishes the multi-factor combinatorial enumeration and the principle that systems rarely decompose into additive single-factor effects.

• Falsifiability
  • Statistics: A null hypothesis can be rejected by sufficiently improbable data but never accepted — only "failed to reject" — a directional asymmetry Fisher (1935) built into the logic of significance testing.

    This sourceStates the directional asymmetry of significance testing directly: 'the null hypothesis is never proved or established, but is possibly disproved, in the course of experimentation' (p. 18); every experiment exists to give the facts a chance of disproving the null.

• Intervention
  • Randomization is the physical realization of \(\mathrm{do}\): assigning \(X\) by coin flip severs every incoming edge at once — including confounders nobody named — which is why it sits at the apex of identification.

    This sourceFoundational argument that randomization severs treatment from all pre-existing common causes — known and unknown — placing it at the apex of causal identification.

• Nonparametric Methods

  • This sourceOliver and Boyd, Edinburgh. (Foundational treatise on experimental design; establishes randomization as the "reasoned basis for inference" and develops the principles of randomization, replication, and blocking that underpin modern randomization-based causal inference.)

• Randomization
  • 1. The random treatment-assignment mechanism — a stochastic procedure with pre-specified, known allocation probabilities that assigns units independent of measured or unmeasured characteristics, ensuring exchangeability ex-ante

    This sourceOliver and Boyd, Edinburgh. (Foundational treatise on experimental design; establishes randomization as the "reasoned basis for inference" and develops the principles of randomization, replication, and blocking that underpin modern randomization-based causal inference.)

• Randomness
  • . Statistics and experimental design use randomness as the foundation of causal inference, formalized by Fisher (1935)

    This sourceOliver and Boyd, Edinburgh. (Foundational treatise on experimental design; establishes randomization as the "reasoned basis for inference" and develops the principles of randomization, replication, and blocking that underpin modern randomization-based causal inference.)

• Reproducibility & Replicability
  • The multi_origin_equal flag is warranted

    This sourceOliver and Boyd, Edinburgh. (Foundational treatise on experimental design; establishes randomization as the "reasoned basis for inference" and develops the principles of randomization, replication, and blocking that underpin modern randomization-based causal inference.)

• Statistical Inference
  • A core function of statistical inference, as Fisher (1935) made central in his treatise on experimental design, is to name the gap between what we observe (a finite, noisy sample) and what we want to know (the true population parameter, causal effect, or mechanism).

    This sourceOliver and Boyd, Edinburgh. (Foundational treatise on experimental design; establishes randomization as the "reasoned basis for inference" and develops the principles of randomization, replication, and blocking that underpin modern randomization-based causal inference.)

Domain-specific

• Null distribution
  • For a composite null, valid calibration may require a nuisance-parameter adjustment, conditioning argument, pivotal statistic, or supremum over null parameter values rather than substitution of one convenient point.

    This sourceOliver and Boyd, foundational treatment of randomization and significance testing.

Mechanisms

• Blocking or Stratification
  • The discipline that guards against this is to pre-specify the blocks

    This sourceDevelops randomized-block designs that form blocks before treatment allocation to control known nuisance variation.

• Factorial Experiment
  • Its failure mode is combinatorial explosion: cells grow as the product of every factor's levels, so a handful of many-level factors becomes thousands of runs, and powering the interaction term needs still more.

    This sourceIts cost is that the number of cells is the product of the factors' level counts, which is why fractional and pairwise reductions exist.

• Randomized Assignment
  • Its strength is manufacturing comparability: in expectation it strips out confounding — measured and unmeasured — so a difference in outcome is attributable to the condition, which is the foundation of credible causal comparison.

Verification

This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.

Links previously used in the corpus

Before the registry existed this work was also linked 3 other ways.

• https://search.worldcat.org/title/The-design-of-experiments/oclc/2417943 ×1
• https://www.biodiversitylibrary.org/bibliography/8131 ×1
• https://www.google.com/books/edition/The_Design_of_Experiments/-EsNAQAAIAAJ ×1

Registry ID ref:e125e11b6787 · see in the full table