قارن الطرق

	تحليل القوة لاختبارات النسب	اختبار ذي الحدين الدقيق	اختبار مربع كاي للاستقلال
المجال	الإحصاء	الإحصاء	الإحصاء
العائلة	Hypothesis test	Regression model	Hypothesis test
سنة النشأة	1988	1988	1900
صاحب الطريقة	Jacob Cohen	Classical exact test; textbook treatment by Siegel & Castellan	Karl Pearson
النوع	Sample size determination	Exact one-sample test for a proportion	Nonparametric test of association
المصدر التأسيسي	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	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.
الأسماء البديلة	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	chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi
ذات صلة	3	2	2
الملخص	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).	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.