Salīdzināt metodes

	P vērtība un statistiskā nozīmība	Problēma ar vairākkārtējiem salīdzinājumiem
Nozare	Pētniecības statistika	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1925	1935
Autors	Ronald Fisher	Carlo Bonferroni; Benjamini & Hochberg
Tips	Concept	Concept
Pirmavots	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.	Bonferroni, C. E. (1935). Il calcolo dei coefficienti di correlazione nel caso di variabilità di gruppi. Instituto Italiano di Statistica.
Citi nosaukumi	p-value, significance test, statistical significance, alpha level	multiple testing, family-wise error, p-value adjustment, false discovery rate
Saistītās	5	4
Kopsavilkums	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).	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.