Methoden vergleichen

	Chi-Quadrat-Unabhängigkeitstest	Fishers Exakter Test	Logistische Regression
Fachgebiet	Statistik	Statistik	Forschungsstatistik
Familie	Hypothesis test	Hypothesis test	Process / pipeline
Entstehungsjahr	1900	1922	1958
Urheber	Karl Pearson	R. A. Fisher	David Roxbee Cox
Typ	Nonparametric test of association	Exact test of independence for categorical data	Method
Wegweisende Quelle	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.	Fisher, R. A. (1922). On the interpretation of chi-squared from contingency tables, and the calculation of P. Journal of the Royal Statistical Society, 85(1), 87–94.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Aliasnamen	chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi	Fisher-Irwin test, exact test of independence, Fisher'ın Kesin Testi	logit model, binomial logistic regression, LR
Verwandt	2	2	3
Zusammenfassung	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.	Fisher's exact test is a nonparametric exact-probability test of independence for small-sample contingency tables, introduced by R. A. Fisher in 1922. Rather than relying on a large-sample approximation, it computes the exact probability of the observed table directly from the hypergeometric distribution.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.