विधियों की तुलना करें

	अनुपात परीक्षणों के लिए शक्ति विश्लेषण	सटीक द्विपद परीक्षण (Exact Binomial Test)	ANOVA के लिए शक्ति विश्लेषण
क्षेत्र	सांख्यिकी	सांख्यिकी	सांख्यिकी
परिवार	Hypothesis test	Regression model	Hypothesis test
उद्भव वर्ष	1988	1988	1988
प्रवर्तक	Jacob Cohen	Classical exact test; textbook treatment by Siegel & Castellan	Jacob Cohen
प्रकार	Sample size determination	Exact one-sample test for a proportion	Sample size determination
मौलिक स्रोत	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
उपनाम	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
संबंधित	3	2	4
सारांश	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.