Compara mètodes

	Problema de les comparacions múltiples	Valor p i significació estadística
Camp	Estadística per a la recerca	Estadística per a la recerca
Família	Process / pipeline	Process / pipeline
Any d'origen	1935	1925
Autor original	Carlo Bonferroni; Benjamini & Hochberg	Ronald Fisher
Tipus	Concept	Concept
Font seminal	Bonferroni, C. E. (1935). Il calcolo dei coefficienti di correlazione nel caso di variabilità di gruppi. Instituto Italiano di Statistica.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.
Àlies	multiple testing, family-wise error, p-value adjustment, false discovery rate	p-value, significance test, statistical significance, alpha level
Relacionats	4	5
Resum	When conducting multiple statistical tests, the probability of obtaining at least one false positive by chance increases with the number of tests. The multiple comparisons problem (also called the multiplicity problem) occurs because if you conduct 100 hypothesis tests at α = 0.05, you expect ~5 false positives by chance alone, even if all null hypotheses are true. Correction methods—Bonferroni, Benjamini-Hochberg false discovery rate (FDR), and others—adjust the significance threshold or p-values to control error rates. This concept is critical for research integrity and has profound implications for exploratory science.	The p-value is the probability of observing data as extreme as or more extreme than what was actually observed, assuming the null hypothesis is true. Introduced by Ronald Fisher in 1925, it is the foundation of frequentist hypothesis testing. Statistical significance is declared when the p-value falls below a pre-specified threshold (alpha level, typically 0.05).