Fisher's exact test is a statistical significance test used in the analysis of contingency tables. Although in practice it is employed when sample sizes are small, it is valid for all sample sizes.
With large samples, a chi-squared test can be a good choice, but the significance value it provides is only an approximation.
The approximation is inadequate when sample sizes are small, or the data are very unequally distributed among the cells of the table, resulting in the cell counts predicted on the null hypothesis (the “expected values”) being low. The usual rule of thumb for deciding whether the chi-squared approximation is good enough is that the chi-squared test is not suitable when the expected values in 20% or more of the cells of a contingency table are below 5.
事实上，卡方的连续性校正方法（Yates's correction for continuity, 1934），多数情况下矫枉过正。而Fisher's exact test是更好的选择，只不过以前限于计算能力，这个“exact test”的方法不容易实现，而现在则不存在这个障碍。