Salīdzināt metodes

	Analīze statistiskajai jaudai attiecību testiem	Hipotēzes neatkarības $\chi^2$ tests
Nozare	Statistika	Statistika
Saime	Hypothesis test	Hypothesis test
Izcelsmes gads	1988	1900
Autors	Jacob Cohen	Karl Pearson
Tips	Sample size determination	Nonparametric test of association
Pirmavots	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.
Citi nosaukumi	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
Saistītās	3	2
Kopsavilkums	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.