Salīdzināt metodes

	Statistiskās jaudas analīze Pīrsona korelācijai	Pīrsona momentu korelācijas koeficients
Nozare	Statistika	Statistika
Saime	Hypothesis test	Hypothesis test
Izcelsmes gads	1988	1895
Autors	Jacob Cohen	Karl Pearson
Tips	Sample size / power determination	Parametric correlation
Pirmavots	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
Citi nosaukumi	Korelasyon Güç Analizi, power analysis for r, sample size for correlation	pearson r, product-moment correlation, bivariate correlation, Pearson Korelasyon Analizi
Saistītās	4	4
Kopsavilkums	Correlation power analysis is a pre-study calculation that determines how many participants are needed — or how much statistical power an existing sample provides — for a Pearson correlation test. Formalised by Jacob Cohen in his landmark 1988 text, it uses the expected correlation coefficient r directly as the effect size, so researchers can plan studies that are neither underpowered nor wastefully large.	The Pearson product-moment correlation coefficient (r) is a parametric measure of the direction and strength of the linear association between two continuous variables. Introduced by Karl Pearson in 1895, it remains the most widely used bivariate correlation statistic in the social, health, and natural sciences. The coefficient ranges from −1 (perfect negative linear relationship) to +1 (perfect positive), with 0 indicating no linear association.