The classical Efron’s bootstrap is a widely used tool in statistical inference. However, because of its disadvantages, many other resampling algorithms were proposed in the literature, especially for the real-valued data. In this paper, we consider three resampling methods for the special case of the interval real-valued data. They were inspired by the smoothed bootstrap and special algorithms known for fuzzy numbers. Using numerical simulations and statistical tools, the introduced methods are compared with the Efron’s bootstrap. It seems that these new algorithms produce samples that can be considered as “similar, but not exactly the same” as the initial data, which is an important aim in the case of resampling methods.