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On the mathematical foundations of theoretical statistics

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Fisher, R. A.·1922·Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character

Abstract

Several reasons have contributed to the prolonged neglect into which the study of statistics, in its theoretical aspects, has fallen. In spite of the immense amount of fruitful labour which has been expended in its practical applications, the basic principles of this organ of science are still in a state of obscurity, and it cannot be denied that, during the recent rapid development of practical methods, fundamental problems have been ignored and fundamental paradoxes left unresolved. This anomalous state of statistical science is strikingly exemplified by a recent paper entitled "The Fundamental Problem of Practical Statistics," in which one of the most eminent of modern statisticians presents what purports to be a general proof of BAYES' postulate, a proof which, in the opinion of a second statistician of equal eminence, "seems to rest upon a very peculiar -- not to say hardly supposable -- relation."

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Metadata

Authors
Fisher, R. A.
Venue
Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character
Published
1922-01-01
Volume
222
Issue
594-604
Pages
309-368
ISSN
  • 0264-3952 (ISSN-L)
  • 2053-9258
OpenAlex
W2084840427
Cited by
4,039
References
0
FWCI
22.26
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: On the mathematical foundations of theoretical statistics. https://app.cochat.ai/papers/10.1098/rsta.1922.0009
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Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character. https://doi.org/10.1098/rsta.1922.0009

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.