Σύγκριση μεθόδων

	Ανάλυση Στατιστικής Ισχύος για τον Συντελεστή Συσχέτισης Pearson	Ανάλυση Ισχύος για Πολλαπλή Παλινδρόμηση
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Hypothesis test	Hypothesis test
Έτος προέλευσης	1988	1988
Δημιουργός	Jacob Cohen	Jacob Cohen
Τύπος	Sample size / power determination	A priori sample size determination
Θεμελιώδης πηγή	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. ISBN: 978-0805802832
Εναλλακτικές ονομασίες	Korelasyon Güç Analizi, power analysis for r, sample size for correlation	regression power analysis, sample size estimation regression, f² power analysis, Güç Analizi — Regresyon
Συναφείς	4	4
Σύνοψη	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.	Power analysis for multiple regression is a pre-study procedure, formalised by Jacob Cohen (1988), that calculates the minimum sample size needed to detect a regression effect of a given size with adequate statistical power. It uses the anticipated R² (or the equivalent Cohen's f² effect size) and the number of predictors to determine how many observations must be collected before data collection begins.