Chi-square vs. Fisher's Exact Test
Meeting chi-square assumption of at least five observations per cell
There is a fundamental difference between chi-square and Fisher's Exact test. They are often used interchangeably both in everyday empirical discourse and also in the literature. There are many calculators available for free on the internet that will calculate inferential statistics for chi-square tests of independence and fisher's exact test. Without the proper statistical competencies, researchers can employ the wrong test. Here is how to know which of these tests to use with your research data:
1. Chi-square - This non-parametric test is used when you are looking at the association between dichotomous categorical variables. The primary inference yielded from this test is the unadjusted odds ratio with 95% confidence interval. EACH CELL of the 2x2 table MUST have at least five observations.
2. Fisher's Exact Test - This non-parametric test is employed when you are looking at the association between dichotomous categorical variables. The primary inference here is also the unadjusted odds ratio with 95% confidence interval. However, the Fisher's Exact Test is used instead of chi-square if ONE OF THE CELLS in the 2x2 has LESS than five observations.
1. Chi-square - This non-parametric test is used when you are looking at the association between dichotomous categorical variables. The primary inference yielded from this test is the unadjusted odds ratio with 95% confidence interval. EACH CELL of the 2x2 table MUST have at least five observations.
2. Fisher's Exact Test - This non-parametric test is employed when you are looking at the association between dichotomous categorical variables. The primary inference here is also the unadjusted odds ratio with 95% confidence interval. However, the Fisher's Exact Test is used instead of chi-square if ONE OF THE CELLS in the 2x2 has LESS than five observations.
