Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Benjamini-Hochberg-menetelmä (FDR-kontrolli)× | Bonferroni-korjaus× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Hypothesis test | Hypothesis test |
| Syntyvuosi≠ | 1995 | 1961 |
| Kehittäjä≠ | Yoav Benjamini & Yosef Hochberg | Carlo Emilio Bonferroni; formalized for multiple comparisons by Olive Jean Dunn |
| Tyyppi≠ | False discovery rate (FDR) procedure | Family-wise error rate (FWER) correction |
| Alkuperäislähde≠ | Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57(1), 289–300. DOI ↗ | Bonferroni, C. E. (1936). Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8, 3–62. link ↗ |
| Rinnakkaisnimet≠ | BH procedure, FDR control, false discovery rate procedure, Benjamini-Hochberg düzeltmesi | Bonferroni adjustment, Bonferroni method, Bonferroni procedure, FWER correction |
| Liittyvät≠ | 3 | 5 |
| Tiivistelmä≠ | The Benjamini-Hochberg (BH) procedure, introduced by Yoav Benjamini and Yosef Hochberg in 1995, controls the false discovery rate (FDR) — the expected proportion of false positives among all rejected hypotheses — rather than the probability of any false positive. By tolerating a controlled fraction of false discoveries, it delivers far greater power than family-wise error rate methods such as Bonferroni or Holm, which is why it has become the standard tool for large-scale simultaneous testing in genomics, neuroimaging, and other high-throughput fields. | The Bonferroni correction is a conservative, universally applicable method for controlling the family-wise error rate (FWER) when conducting multiple simultaneous hypothesis tests. Grounded in Bonferroni's 1936 probability inequality and formalized for multiple comparisons by Olive Jean Dunn in 1961, the procedure divides the target significance level α by the number of tests m, ensuring that the probability of making even one false rejection across the entire family of tests does not exceed α. |
| ScholarGateAineisto ↗ |
|
|