Porównaj metody

	Analiza mocy dla testów proporcji	Dokładny test dwumianowy	Test niezależności chi-kwadrat
Dziedzina	Statystyka	Statystyka	Statystyka
Rodzina	Hypothesis test	Regression model	Hypothesis test
Rok powstania	1988	1988	1900
Twórca	Jacob Cohen	Classical exact test; textbook treatment by Siegel & Castellan	Karl Pearson
Typ	Sample size determination	Exact one-sample test for a proportion	Nonparametric test of association
Źródło pierwotne	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.
Inne nazwy	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
Pokrewne	3	2	2
Podsumowanie	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.