Salīdzināt metodes

	Statistiskās jaudas analīze Pīrsona korelācijai	Spīrmena ranga sakarības koeficients
Nozare	Statistika	Statistika
Saime	Hypothesis test	Hypothesis test
Izcelsmes gads	1988	1904
Autors	Jacob Cohen	Charles Spearman
Tips	Sample size / power determination	Nonparametric rank-based correlation
Pirmavots	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832	Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101.
Citi nosaukumi	Korelasyon Güç Analizi, power analysis for r, sample size for correlation	Spearman's rho, Spearman rank-order correlation, Spearman Sıra Korelasyonu
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 Spearman rank correlation coefficient (ρ) is a nonparametric measure of the monotonic association between two variables. Introduced by Charles Spearman in 1904, it converts raw observations to ranks and measures how consistently one variable increases as the other increases, without assuming a normal distribution or a linear relationship.