방법 비교

	비율 검정에 대한 검정력 분석	카이제곱 독립성 검정	ANOVA를 위한 검정력 분석
분야	통계학	통계학	통계학
계열	Hypothesis test	Hypothesis test	Hypothesis test
기원 연도	1988	1900	1988
창시자	Jacob Cohen	Karl Pearson	Jacob Cohen
유형	Sample size determination	Nonparametric test of association	Sample size determination
원전	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.	Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
별칭	proportion power analysis, two-proportion z-test power, z-test for proportions power, Oran Testi Güç Analizi	chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi	ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA
관련	3	2	4
요약	Power analysis for proportion tests is a prospective sample-size planning method used to determine how many participants are needed to detect a meaningful difference between two (or one) proportions with a specified probability. Formalised by Jacob Cohen in his 1988 landmark text, it applies the arcsine transformation to convert proportions into the effect-size index h, enabling direct calculation of the required sample size.	The chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900.	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.