Salīdzināt metodes

	P vērtība un statistiskā nozīmība	Efekta lielums
Nozare	Pētniecības statistika	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1925	1988
Autors	Ronald Fisher	Jacob Cohen
Tips	Concept	Concept
Pirmavots	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5
Citi nosaukumi	p-value, significance test, statistical significance, alpha level	ES, Cohen's d, standardized effect, practical significance
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).	Effect size quantifies the magnitude of a research finding independent of sample size. While a p-value tells you whether a result is statistically significant, an effect size tells you how big the result is. Jacob Cohen formalized effect size measurement in behavioral sciences (1988), establishing standard benchmarks (small = 0.2, medium = 0.5, large = 0.8 for Cohen's d). Effect sizes are essential for meta-analysis, power analysis, and communicating the practical importance of research findings.