Search results
Results from the WOW.Com Content Network
This formula is chiefly used when at least one cell of the table has an expected count smaller than 5. ∑ i = 1 N O i = 20 {\displaystyle \sum _{i=1}^{N}O_{i}=20\,} The following is Yates's corrected version of Pearson's chi-squared statistics :
The example above is the simplest kind of contingency table, a table in which each variable has only two levels; this is called a 2 × 2 contingency table. In principle, any number of rows and columns may be used. There may also be more than two variables, but higher order contingency tables are difficult to represent visually.
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.
Under pressure from Fisher, Barnard retracted his test in a published paper, [8] however many researchers prefer Barnard’s exact test over Fisher's exact test for analyzing 2 × 2 contingency tables, [9] since its statistics are more powerful for the vast majority of experimental designs, whereas Fisher’s exact test statistics are conservative, meaning the significance shown by its p ...
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").
For , and matrices of size the two methods produces the same transformed table provided ranks the contingency tables the same as the scalar-valued Liu-Lu index does. [20] However, for Z {\displaystyle {Z}} matrices larger than 2×2, the generalized Liu-Lu index is matrix-valued, so it is different from the scalar-valued v ( Z ) {\displaystyle v ...
Four bits of information determine all the entries in the contingency table, including its marginal totals. For example, if we know H, M, F, and C, then we can compute all the marginal totals for any threshold. Alternatively, if we know H/P, F/Q, P, and Q, then we can compute all the entries in the table. [1]
Given two jointly distributed random variables and , the conditional probability distribution of given is the probability distribution of when is known to be a particular value; in some cases the conditional probabilities may be expressed as functions containing the unspecified value of as a parameter.