Statistical Methods for Research Workers¶
Fisher, R. A. (1925). Statistical Methods for Research Workers.
Cited by¶
12 citations across 11 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Aggregation
- Aggregation collapses many items into a unified form that retains chosen features while suppressing granular detail, formalized in classical statistics as the reduction of a sample to a summary statistic (Fisher, 1925).
This sourceOliver & Boyd, Edinburgh. Foundational statistics text introducing summary statistics and the reduction of a sample to a summary; supports the claim that the reduction of a sample to a summary statistic is formalized in classical statistics.
- Aggregation pervades statistical analysis, social choice, economic accounting, machine learning, ecology, and organizational reporting; despite differing vocabularies, the operation is structurally identical—reducing a multiset of inputs to a single representative summary—as documented across Fisher's (1925) statistical foundations and the literatures that followed.
This sourceOliver and Boyd, Edinburgh. Establishes aggregation operations (means, variances, sufficient statistics) recurring across statistics and downstream fields; supports the broad-use claim that the operation is structurally identical across domains.
- Aggregation collapses many items into a unified form that retains chosen features while suppressing granular detail, formalized in classical statistics as the reduction of a sample to a summary statistic (Fisher, 1925).
- Blocking (In Experimental Design)
- Blocking exemplifies a deep design principle: when known structure exists in the population of experimental units, incorporating that structure into the design is more efficient than ignoring it and hoping randomization alone will balance it
This sourceOliver & Boyd, Edinburgh. The first text to present analysis of variance together with randomization and blocking, establishing that incorporating known structure into the design is more efficient than ignoring it and relying on randomization alone.
- Blocking exemplifies a deep design principle: when known structure exists in the population of experimental units, incorporating that structure into the design is more efficient than ignoring it and hoping randomization alone will balance it
- Distributional Assumption
- A distributional assumption is a structural commitment to assume that uncertain quantities follow a specific probability distribution or shape family (normal, exponential, power-law, etc.) when modeling unknown or variable data, as Fisher (1925) systematized in his foundational treatment of parametric inference.
This sourceOliver & Boyd, Edinburgh. Foundational text of modern parametric inference: introduces likelihood-based estimation and significance testing built on assumed sampling distributions (the normal, the t, χ², and F families), systematizing the practice of committing data to a specific distributional form to enable inference.
- A distributional assumption is a structural commitment to assume that uncertain quantities follow a specific probability distribution or shape family (normal, exponential, power-law, etc.) when modeling unknown or variable data, as Fisher (1925) systematized in his foundational treatment of parametric inference.
- Experimental Design
- Hypothesis Testing (Null vs. Alternative)
- The modern framework synthesizes Ronald Fisher's continuous evidential tradition
This sourceOliver & Boyd. Establishes the formal statistical concept of an unbiased estimator and the use of randomization to enforce identity-invariance in experimental design; the metrology-furthest realization of the prime — invariance under sample identity stated in purely mathematical terms with no parties or preferences.
- The modern framework synthesizes Ronald Fisher's continuous evidential tradition
- Impartiality
- That breadth — the fact that the pattern shows up cleanly in measurement physics, where there are no parties with preferences at all — is what makes impartiality a prime rather than a specialty of ethics or law.
This sourceOliver & Boyd. Establishes the formal statistical concept of an unbiased estimator and the use of randomization to enforce identity-invariance in experimental design; the metrology-furthest realization of the prime — invariance under sample identity stated in purely mathematical terms with no parties or preferences.
- That breadth — the fact that the pattern shows up cleanly in measurement physics, where there are no parties with preferences at all — is what makes impartiality a prime rather than a specialty of ethics or law.
- Randomization
- Names the specific procedure — random assignment with specified probabilities, independent of unit characteristics — that provides the unique inferential warrant for causal claims in controlled experiments
This sourceOliver & Boyd. Establishes the formal statistical concept of an unbiased estimator and the use of randomization to enforce identity-invariance in experimental design; the metrology-furthest realization of the prime — invariance under sample identity stated in purely mathematical terms with no parties or preferences.
- Names the specific procedure — random assignment with specified probabilities, independent of unit characteristics — that provides the unique inferential warrant for causal claims in controlled experiments
- Statistical Inference
- The core move, articulated already in Fisher (1925), is to treat what is observed (a sample) as one instance drawn from a probability distribution over possible samples, and to ask: what do the data tell us about the true parameters, unobserved causal structures, or future outcomes?
This sourceOliver & Boyd. Establishes the formal statistical concept of an unbiased estimator and the use of randomization to enforce identity-invariance in experimental design; the metrology-furthest realization of the prime — invariance under sample identity stated in purely mathematical terms with no parties or preferences.
- The core move, articulated already in Fisher (1925), is to treat what is observed (a sample) as one instance drawn from a probability distribution over possible samples, and to ask: what do the data tell us about the true parameters, unobserved causal structures, or future outcomes?
- Statistical Significance (p-Value)
- Cross-domain transfer is productive: multiplicity-adjustment methods from genomics to large-scale A/B testing; sequential-testing methods from clinical trials to tech experimentation; meta-analytic combination methods from medicine to psychology to economics; permutation-based p-values from agriculture to neuroscience
This sourceOliver & Boyd. Establishes the formal statistical concept of an unbiased estimator and the use of randomization to enforce identity-invariance in experimental design; the metrology-furthest realization of the prime — invariance under sample identity stated in purely mathematical terms with no parties or preferences.
- Cross-domain transfer is productive: multiplicity-adjustment methods from genomics to large-scale A/B testing; sequential-testing methods from clinical trials to tech experimentation; meta-analytic combination methods from medicine to psychology to economics; permutation-based p-values from agriculture to neuroscience
- Type I & Type II Errors
This sourceOliver & Boyd. Establishes the formal statistical concept of an unbiased estimator and the use of randomization to enforce identity-invariance in experimental design; the metrology-furthest realization of the prime — invariance under sample identity stated in purely mathematical terms with no parties or preferences.
- Variability
- The essential commitment is that variation is itself structured and informative: its magnitude, shape, and sources carry content about the system producing it, and variability analysis separates signal from noise, between-group from within-group differences, and reducible from irreducible spread
This sourceOliver & Boyd. Establishes the formal statistical concept of an unbiased estimator and the use of randomization to enforce identity-invariance in experimental design; the metrology-furthest realization of the prime — invariance under sample identity stated in purely mathematical terms with no parties or preferences.
- The essential commitment is that variation is itself structured and informative: its magnitude, shape, and sources carry content about the system producing it, and variability analysis separates signal from noise, between-group from within-group differences, and reducible from irreducible spread
Verification¶
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Links previously used in the corpus¶
Before the registry existed this work was also linked 3 other ways.
- https://www.biodiversitylibrary.org/bibliography/85007 ×2
- https://www.cambridge.org/core/journals/journal-of-the-institute-of-actuaries/article/abs/statistical-methods-for-research-workers-by-r-a-fisher-pp-239-ix-vi-tables-edinburgh-and-london-oliver-boyd-1925-price-15s-the-fundamentals-of-statistics-by-l-l-thurstone-pp-237-xvi-new-york-the-macmillan-company-1925-price-8s-6d/DA584CC1A950F4BF226ED1AEADDF23C4 ×1
- https://www.gutenberg.org/ebooks/65622 ×1
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