G–test" near the bottom of this page, pick either chi-square or G–test, then stick with that choice for the rest of your life. ![]() You should read the section on "Chi-square vs. The G–test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar. See the web page on small sample sizes for discussion of what "small" means. If the expected number of observations in any category is too small, the G–test may give inaccurate results, and you should use an exact test instead. You compare the observed counts of numbers of observations in each category with the expected counts, which you calculate using some kind of theoretical expectation (such as a 1:1 sex ratio or a 1:2:1 ratio in a genetic cross). ![]() Use the G–test of goodness-of-fit when you have one nominal variable with two or more values (such as male and female, or red, pink and white flowers). You use the G–test of goodness-of-fit (also known as the likelihood ratio test, the log-likelihood ratio test, or the G 2 test) when you have one nominal variable, you want to see whether the number of observations in each category fits a theoretical expectation, and the sample size is large.
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