Порівняння методів

	Аналіз потужності для тестів пропорцій	Точний біноміальний тест	Аналіз потужності для дисперсійного аналізу
Галузь	Статистика	Статистика	Статистика
Родина	Hypothesis test	Regression model	Hypothesis test
Рік появи	1988	1988	1988
Автор методу	Jacob Cohen	Classical exact test; textbook treatment by Siegel & Castellan	Jacob Cohen
Тип	Sample size determination	Exact one-sample test for a proportion	Sample size determination
Основоположне джерело	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.	Siegel, S. & Castellan, N. J. (1988). Nonparametric Statistics for the Behavioral Sciences (2nd ed.). McGraw-Hill. ISBN: 978-0070573574	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Інші назви	proportion power analysis, two-proportion z-test power, z-test for proportions power, Oran Testi Güç Analizi	exact binomial test, binomial probability test, exact test for a proportion, Tam Binom Testi	ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA
Пов'язані	3	2	4
Підсумок	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 exact binomial test checks whether the observed number of successes in a fixed number of independent trials is consistent with a pre-specified success probability p₀. Because it computes exact binomial tail probabilities rather than relying on a normal approximation, it is the gold standard for testing a proportion in small samples; this two-sided formulation follows Siegel & Castellan's classic treatment (1988).	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.