  Hypothesis testing in contingency tables is usually based on asymptotic
results, thereby restricting its proper use to large samples. To study these
tests in small samples, we consider the likelihood ratio test and define an
accurate index, the P-value, for the celebrated hypotheses of homogeneity,
independence, and Hardy-Weinberg equilibrium. The aim is to understand the use
of the asymptotic results of the frequentist Likelihood Ratio Test and the
Bayesian FBST -- Full Bayesian Significance Test -- under small-sample
scenarios. The proposed exact P-value is used as a benchmark to understand the
other indices. We perform analysis in different scenarios, considering
different sample sizes and different table dimensions. The exact Fisher test
for $2 \times 2$ tables that drastically reduces the sample space is also
discussed. The main message of this paper is that all indices have very similar
behavior, so the tests based on asymptotic results are very good to be used in
any circumstance, even with small sample sizes.
