The objective of this study is to compare point estimation methods and interval estimation methods for the Poisson distribution with small sample sizes. Three methods of point estimation: maximum likelihood method, Bayesian method and minimax method, and three methods of interval estimation: normal method, normal-Bayesian method and score-Bayesian method are considered. The lowest mean absolute error and the lowest average width are used as the criteria of selection for point estimation and interval estimation, respectively. The scopes of this study consist of sample sizes: 5, 6, 7, 8, 9, 10 and the parameter lambda is equal to 0.02, 0.04, 0.06, 0.08 and 0.1. Data is simulated 1,000 times generated by using the JAVA software. The results of this research are as follows: For point estimation, we recommend that for all sample sizes and parameter lambda, the Bayesian method should be used. In case of interval estimation, normal-Bayesian method is recommended for sample sizes are 5 to 8, lambda between 0.2 to 0.4 and sample sizes are 9 to 10, lambda is equal to 0.2 whereas score-Bayesian method should be considered for sample sizes are 5 to 8, values of. ranging from 0.6 to 1 and sample sizes are 9 to 10, lambda between 0.4 to 1.0.
