The bootstrap method is a computer intensive statistical method that is widely used in
performing nonparametric inference. Categorical data analysis, in particular the analysis of
contingency tables, is commonly used in applied field. This work considers nonparametric
bootstrap tests for the analysis of contingency tables. There are only a few research papers
which exploit this field. The p-values of tests in contingency tables are discrete and should
be uniformly distributed under the null hypothesis. The results of this article show that
corresponding bootstrap versions work better than the standard tests. Properties of the
proposed tests are illustrated and discussed using Monte Carlo simulations. This article
concludes with an analytical example that examines the performance of the proposed tests
and the confidence interval of the association coefficient.