The chi-square test is a continuous probability law and at the same time is a nonparametric test which compares qualitative data with the use of small samples in the sub-groups but not in the total number.
In the literature of the various books and statistical courses to make a chi-square, the total number with the different groups must be greater than 30 sufficient to give a statistical conclusion
If it is a number that is proven as a sampling limit why some require a larger sample?
Because that number determine the power of the study. (in terms of statistics.)
The required sample is based on the following issues:
so by considering the above sections you will calculate your sample. for instance if you are going to study rare disease i.e. prevalence
The other answers are wrong to very wrong. First of all, the chi-square test is absolutely a parametric test. This is a vital piece of information. A minimum sample size of 30 is often recommended because of the central limit theorem; however, in actuality there is nothing special about n=30 and some texts recommend 50 or 100 samples.
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