G-test

In statistics, G-tests are likelihood-ratio or maximum likelihood statistical significance tests that are increasingly being used in situations where chi-squared tests were previously recommended. == Formulation == The general formula for test statistics of the G-test is G = 2 ∑ i O i ⋅ ln ⁡ ( O i E i ) , {\displaystyle G=2\sum _{i}{O_{i}\cdot \ln \left({\frac {O_{i}}{E_{i}}}\right)},} where O i ≥ 0 {\displaystyle O_{i}\geq 0} is the observed count in a cell, E i > 0 {\displaystyle E_{i}>0} is the expected count under the null hypothesis, ln {\displaystyle \ln } denotes the natural logarithm, and the sum is taken over all non-empty cells.

Source: Wikipedia — G-test (CC BY-SA 4.0)

G-test

In statistics, G-tests are likelihood-ratio or maximum likelihood statistical significance tests that are increasingly being used in situations where chi-squared tests were previously recommended. == Formulation == The general formula for test statistics of the G-test is G = 2 ∑ i O i ⋅ ln ⁡ ( O i E i ) , {\displaystyle G=2\sum _{i}{O_{i}\cdot \ln \left({\frac {O_{i}}{E_{i}}}\right)},} where O i ≥ 0 {\displaystyle O_{i}\geq 0} is the observed count in a cell, E i > 0 {\displaystyle E_{i}>0} is the expected count under the null hypothesis, ln {\displaystyle \ln } denotes the natural logarithm, and the sum is taken over all non-empty cells.

This neuron ends here.

Source: Wikipedia "G-test" · CC BY-SA 4.0

Share this article: X · Bluesky
Privacy Policy