A new universal likelihood ratio test has been developed that can be used in a wide range of statistical scenarios without needing specific conditions. This test allows statisticians to create valid hypothesis tests in situations where none existed before. By comparing it to the classical likelihood ratio test, researchers found that the universal test performs well, especially when using a repeated subsampling approach. Even in high-dimensional settings, the universal test can provide more accurate results than the traditional test. This new method has been shown to have higher power when testing nonconvex hypotheses, making it a valuable tool for statistical inference.