Vertaile menetelmiä

	Khii toiseen tunnusluvun riippumattomuustesti	ANOVA-analyysi (Power Analysis for ANOVA)
Tieteenala	Tilastotiede	Tilastotiede
Menetelmäperhe	Hypothesis test	Hypothesis test
Syntyvuosi	1900	1988
Kehittäjä	Karl Pearson	Jacob Cohen
Tyyppi	Nonparametric test of association	Sample size determination
Alkuperäislähde	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.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Rinnakkaisnimet	chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi	ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA
Liittyvät	2	4
Tiivistelmä	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.	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.