Sequential Monte Carlo (SMC) samplers are Bayesian inference methods that employ sampling and resampling to generate a population of N weighted samples to estimate the state of a statistical model. This approach is robust and has made SMC commonly used across numerous areas of Statistics and Machine Learning. However, the resampling step alters the distribution of the sample weights, increasing the estimates’ uncertainty. This paper presents Minimum Error Resampling (MER), a resampling scheme which aims at minimizing the Mean Squared Error (MSE) in the distribution of the sample weights before and after resampling. Our theoretical analysis proves that MER achieves this minimum MSE. We present experimental results, in the context of sampling N Bayesian Decision Trees, showing that our proposed MER improves the performance of an SMC sampler when compared to the same sampler using other state-of-the-art resampling methods. The code is available on GitHub as open source.