Bonferroni correction concept
| Definition | Dividing the significance threshold by the number of tests, which holds the family-wise error rate at or below the original threshold whatever the dependence among the tests, at the price of being conservative. Primer S, equation S.16. Also Bonferroni. |
|---|---|
| Example | Forty tests at 0.05 become forty tests at 0.00125. |
| Book | Data Mining as Observation, draft 0.2, commit f3914f0; entry id bonferroni-correction, kind concept. |
| Status | no ledger row names this entry. Corrections: none recorded. |
| Defining equation | Book equation S.16. |
| Assumptions and scope | none |
| Prior art | none recorded |
| Evidence | none |
| Reviewed | semantic review 2026-09-09; generated 2026-09-10 from records at the commits on the provenance page. |
Equation
Book equation S.16.
\[\Pr[\text{at least one false alarm}]\le m\,\alpha,\qquad \alpha_{\mathrm{Bonf}}=\frac{\alpha}{m}.\]
Conditions
none
Ledger
none
First stated
Primer S section S.8 of Data Mining as Observation, added in draft 0.3 (2026-09-09) for the ECE 514 readers whose first courses are far behind. The idea is standard and TSK Appendix C covers it at length.
Measurements
none
Failures and corrections
none
Invariance envelope
none declared
Machine checked
none
Used in
Data Mining as Observation primer S, chapters 5, 8.
Related
family-wise error rate; multiple comparisons.
See also
none
Status
Generated 2026-09-10 by encyclopedia/generate.py; book
at observation-data-mining f3914f0; the commit of every record is listed
in the encyclopedia’s provenance.