Researchers have developed a new method to accurately measure the Wasserstein distance, a mathematical concept used in machine learning. Unlike previous methods, this new approach provides valid confidence intervals for the Wasserstein distance, ensuring accurate results for both simple and complex problems. The method has been tested on various datasets and has shown promising results.