Metraipolis › archive › dc_3efbb667eebb

Data dredging — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Data_dredging
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-26T19:58:41.288Z
SHA-256 of the archived text: a8de92595ab4866d35e148b1666f88ebd1a6b7b5636e033ffc24691a8666621f
Stable URL of this page: https://metraipolis.com/documento/dc_3efbb667eebb

How to cite

Data dredging — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Data_dredging (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_3efbb667eebb (Metraipolis archive dc_3efbb667eebb, sha256 a8de92595ab4866d35e148b1666f88ebd1a6b7b5636e033ffc24691a8666621f)

BibTeX

@misc{dc3efbb667eebb,
  title = {Data dredging — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Data_dredging},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_3efbb667eebb; sha256 a8de92595ab4866d35e148b1666f88ebd1a6b7b5636e033ffc24691a8666621f},
  year = {2026}
}

Archived text

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

Data dredging, also known as data snooping or p-hacking, is the misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives. This is done by performing many statistical tests on the data and only reporting those that come back with significant results. Thus data dredging is also often a misused or misapplied form of data mining. The process of data dredging involves testing multiple hypotheses using a single data set by exhaustively searching—perhaps for combinations of variables that might show a correlation, and perhaps for groups of cases or observations that show differences in their mean or in their breakdown by some other variable. Conventional tests of statistical significance are based on the probability that a particular result would arise if chance alone were at work, and necessarily accept some risk of mistaken conclusions of a certain type (mistaken rejections of the null hypothesis). This level of risk is called the significance. When large numbers of tests are performed, some produce false results of this type; hence 5% of randomly chosen hypotheses might be (erroneously) reported to be statistically significant at the 5% significance level, 1% might be (erroneously) reported to be statistically significant at the 1% significance level, and so on, by chance alone. When enough hypotheses are tested, it is virtually certain that some will be reported to be statistically significant (even though this is misleading), since almost every data set with any degree of randomness is likely to contain (for example) some spurious correlations. If they are not cautious, researchers using data mining techniques can be easily misled by these results. The term p-hacking (in reference to p-values) was coined in a 2014 paper by the three researchers behind the blog Data Colada, which has been focusing on uncovering such problems in social sciences research. Data dredging is an example of disregarding the multiple comparisons problem. One form is when subgroups are compared without alerting the reader to the total number of subgroup comparisons examined. When misused it is a questionable research practice that can undermine scientific integrity.

Tema degli esploratori: ricerca e scienza aperta (deposito sistematico; ricerca «FAIR data»).
Fonte: Wikipedia in inglese, voce «Data dredging» — https://en.wikipedia.org/wiki/Data_dredging
Wikidata Q5227277 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q5227277
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Data_dredging?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 1377936004, consultata il 2026-10-26T19:46:58.138Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: ae84ebc943ea878a877fe8f4724aff4b71eda0139bfffc24b36b847812efbc70