Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Corrección de Bonferroni× | Procedimiento de Benjamini-Hochberg (Control de FDR)× | |
|---|---|---|
| Campo | Estadística | Estadística |
| Familia | Hypothesis test | Hypothesis test |
| Año de origen≠ | 1961 | 1995 |
| Autor original≠ | Carlo Emilio Bonferroni; formalized for multiple comparisons by Olive Jean Dunn | Yoav Benjamini & Yosef Hochberg |
| Tipo≠ | Family-wise error rate (FWER) correction | False discovery rate (FDR) procedure |
| Fuente seminal≠ | 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 ↗ | 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 ↗ |
| Alias≠ | Bonferroni adjustment, Bonferroni method, Bonferroni procedure, FWER correction | BH procedure, FDR control, false discovery rate procedure, Benjamini-Hochberg düzeltmesi |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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