Multiple Hypothesis Testing

Multiple hypothesis testing addresses the challenge of simultaneously testing numerous hypotheses, controlling for the increased risk of false positives. Current research focuses on improving the power and robustness of these tests, particularly when dealing with complex data structures (e.g., high-dimensional data, graphs) and incorporating ancillary information or multiple test statistics to enhance accuracy. This field is crucial for diverse applications, including machine learning model evaluation, scientific discovery (e.g., gene association studies, new physics searches), and policy optimization, where reliably identifying true effects amidst numerous possibilities is paramount. Improved methods are leading to more accurate and reliable conclusions in these areas.