What's the usage difference between G test and Chi-square test? These 2 tests are both test hypothesis that proportions are the same in different nominal variables.
In R, you can find GTest() and chisq.test() for these 2 tests.
Then, when should use G test? When should use Chi-square test?
 A: Both these tests use statistics that are approximately chi-squared-distributed. The larger sample, the better approximation.
If your sample is reasonably large G test and chi-square test behave similarly. But with small samples, G test is better. It's statistic follows distribution that is closer to chi-square distribution than chi-square test's distribution, so calculation of p-value is more acurate.
The obvious question is "How small is small sample?". You can find plenty of definitions, rules of thumb and advices in textbooks. Two, I see most otfen are:


*

*sample is small, when in contingency table, we have at least one cell with observed count less than 5

*sample is small, when in contingency table, we have at least one cell with expected count less than 5.


The latter is used in chisq.test(). If it is met R warns about possible approximation problem:
> chisq.test(cbind(c(2,3), c(4,5)))

        Pearson's Chi-squared test with Yates' continuity correction

data:  cbind(c(2, 3), c(4, 5))
X-squared = 3.8347e-32, df = 1, p-value = 1

Warning message:
In chisq.test(cbind(c(2, 3), c(4, 5))) :
  Chi-squared approximation may be incorrect

