পদ্ধতির তুলনা করুন

	পিয়ার্সন সহসম্বন্ধের জন্য পরিসংখ্যান শক্তি বিশ্লেষণ	একাধিক রিগ্রেশনের জন্য পাওয়ার বিশ্লেষণ
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Hypothesis test	Hypothesis test
উদ্ভবের বছর	1988	1988
প্রবর্তক	Jacob Cohen	Jacob Cohen
ধরন	Sample size / power determination	A priori sample size determination
মৌলিক উৎস	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
অপর নাম	Korelasyon Güç Analizi, power analysis for r, sample size for correlation	regression power analysis, sample size estimation regression, f² power analysis, Güç Analizi — Regresyon
সম্পর্কিত	4	4
সারসংক্ষেপ	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.