Salīdzināt metodes

	Analīze chi-kvadrātiskajai jaudai	Statistiskās jaudas analīze Pīrsona korelācijai	Jaudas analīze ANOVA gadījumā
Nozare	Statistika	Statistika	Statistika
Saime	Hypothesis test	Hypothesis test	Hypothesis test
Izcelsmes gads	1988	1988	1988
Autors	Jacob Cohen	Jacob Cohen	Jacob Cohen
Tips	Sample size and power calculation	Sample size / power determination	Sample size determination
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. ISBN: 978-0805802832	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Citi nosaukumi	chi-square power, chi-square sample size, Ki-Kare Güç Analizi, goodness-of-fit power	Korelasyon Güç Analizi, power analysis for r, sample size for correlation	ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA
Saistītās	2	4	4
Kopsavilkums	Chi-square power analysis is a prospective calculation that determines the minimum sample size required — or the statistical power achievable with a given sample — for chi-square independence tests or goodness-of-fit tests. It rests on Cohen's w effect size framework, codified by Jacob Cohen in his landmark 1988 work on statistical power for the behavioral sciences.	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 ANOVA is a prospective statistical technique that determines the minimum sample size needed to detect a specified group mean difference with a chosen probability. Formalized by Jacob Cohen in his 1988 monograph, it translates a researcher's effect size expectation — expressed as Cohen's f — along with the desired Type I error rate (alpha) and statistical power (1 − beta) into a concrete per-group sample size recommendation for one-way or factorial ANOVA designs.