Metraipolis › archive › dc_3db49e8a366f

Foundations of statistics — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Foundations_of_statistics
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-06T12:38:52.816Z
SHA-256 of the archived text: 8a028ea06374cdc8e027e15cf2071ec6f76bb04a0d6aececd09d741495343b09
Stable URL of this page: https://metraipolis.com/documento/dc_3db49e8a366f

How to cite

Foundations of statistics — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Foundations_of_statistics (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_3db49e8a366f (Metraipolis archive dc_3db49e8a366f, sha256 8a028ea06374cdc8e027e15cf2071ec6f76bb04a0d6aececd09d741495343b09)

BibTeX

@misc{dc3db49e8a366f,
  title = {Foundations of statistics — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Foundations_of_statistics},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_3db49e8a366f; sha256 8a028ea06374cdc8e027e15cf2071ec6f76bb04a0d6aececd09d741495343b09},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/Foundations_of_statistics. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The Foundations of Statistics are the mathematical and philosophical bases for statistical methods. These bases are the theoretical frameworks that ground and justify methods of statistical inference, estimation, hypothesis testing, uncertainty quantification, and the interpretation of statistical conclusions. Further, a foundation can be used to explain statistical paradoxes, provide descriptions of statistical laws, and guide the application of statistics to real-world problems. Different statistical foundations may provide different, contrasting perspectives on the analysis and interpretation of data, and some of these contrasts have been subject to centuries of debate. Examples include the Bayesian inference versus frequentist inference; the distinction between Fisher's significance testing and the Neyman-Pearson hypothesis testing; and whether the likelihood principle holds. Certain frameworks may be preferred for specific applications, such as the use of Bayesian methods in fitting complex ecological models. Bandyopadhyay & Forster identify four statistical paradigms: classical statistics (error statistics), Bayesian statistics, likelihood-based statistics, and information-based statistics using the Akaike Information Criterion. More recently, Judea Pearl reintroduced formal mathematics by attributing causality in statistical systems that addressed the fundamental limitations of both Bayesian and Neyman-Pearson methods, as discussed in his book Causality.

Tema degli esploratori: statistica e scienza dei dati (deposito sistematico; ricerca «statistical inference»).
Fonte: Wikipedia in inglese, voce «Foundations of statistics» — https://en.wikipedia.org/wiki/Foundations_of_statistics
Wikidata Q5474576: «concern the epistemological debate in statistics over how one should conduct inductive inference from data» (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q5474576
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Foundations_of_statistics?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1378320476, consultata il 2026-10-06T10:54:49.543Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: 05873e4229df6e4918cb5d046f772af552f3787df095c5150ba6c98118e813c1