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In statistics, a contingency table (also known as a cross tabulation or crosstab) is a type of table in a matrix format that displays the multivariate frequency distribution of the variables. They are heavily used in survey research, business intelligence, engineering, and scientific research.
Fisher's exact test (also Fisher-Irwin test) is a statistical significance test used in the analysis of contingency tables. [1] [2] [3] Although in practice it is employed when sample sizes are small, it is valid for all sample sizes.
Boschloo's test is a statistical hypothesis test for analysing 2x2 contingency tables. It examines the association of two Bernoulli distributed random variables and is a uniformly more powerful alternative to Fisher's exact test. It was proposed in 1970 by R. D. Boschloo. [1]
Contingency was especially emphasized by Stephen Jay Gould, particularly in his 1989 book Wonderful Life. [ 1 ] [ 2 ] Gould used the thought experiment of rewinding the "tape of life" to the distant past, and argued that even small changes to history would result in evolutionary outcomes very different from our world.
McNemar's test is a statistical test used on paired nominal data.It is applied to 2 × 2 contingency tables with a dichotomous trait, with matched pairs of subjects, to determine whether the row and column marginal frequencies are equal (that is, whether there is "marginal homogeneity").
A 2 x 2 contingency table is used at each interval of k comparing both groups on an individual item. The rows of the contingency table correspond to group membership (reference or focal) while the columns correspond to correct or incorrect responses. The following table presents the general form for a single item at the kth ability interval.
Statistical tests for contingency tables (10 P) Summary statistics for contingency tables (28 P) Pages in category "Contingency table"
In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or r φ) is a measure of association for two binary variables.. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.