A new test has been developed to compare average values in large datasets, without needing strict assumptions about the data. The test uses a mix of chi-square distributions to approximate the null distribution, resulting in a more accurate assessment. When tested with simulations, the new method showed better control over errors compared to existing tests. This approach can help researchers analyze high-dimensional data more effectively.