Rethinking the Assumptions of Chi-squared and Fisher’s Exact Tests

Abstract

Almost always when we are confronted with a contingency table, we summon to mind the chi-squared ( ) test, which was developed more than a century ago [1]. Like most classic statistical tests, the test can be conducted with pencil and paper, so long as the raw data are summarized; it is seemingly straightforward and even considered one of the easiest analyses, although this is not true. Several complex modern statistical methods may analyze contingency tables in a more sophisticated way, but they do not add much practical benefit in most situations, especially in the healthcare field. Thus, the test has been one of our closest friends for a long time, as it deserves to be.