Sammenlign metoder

	Eksakt binomialtest	Chi-kvadrat-test for uafhængighed	Poweranalyse for ANOVA
Fagområde	Statistik	Statistik	Statistik
Familie	Regression model	Hypothesis test	Hypothesis test
Oprindelsesår	1988	1900	1988
Ophavsperson	Classical exact test; textbook treatment by Siegel & Castellan	Karl Pearson	Jacob Cohen
Type	Exact one-sample test for a proportion	Nonparametric test of association	Sample size determination
Oprindelig kilde	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.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Aliasser	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	ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA
Relaterede	2	2	4
Resumé	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.	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.