In
statistics, G-tests are
likelihood-ratio or
maximum likelihood statistical significance tests that are increasingly being used in situations where
chi-square tests were previously recommended.The commonly used chi-squared tests for goodness of fit to a distribution and for independence in
contingency tables are in fact approximations of the
log-likelihood ratio on which the G-tests are based. This approximation was developed by
Karl Pearson because at the time it was unduly laborious to calculate log-likelihood ratios. With the advent of electronic calculators and personal computers, this is no longer a problem. G-tests are coming into increasing use, particularly since they were recommended in the 1994 edition of the popular statistics text book by Sokal and Rohlf. Dunning introduced the test to the computational linguistics community where it is now widely used.
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