Test Statistic

A test statistic is a numerical summary of data used to assess a hypothesis, often by comparing its observed value to a known probability distribution. Current research focuses on improving the accuracy and efficiency of calculating and modeling the distribution of test statistics, including developing novel algorithms for exact calculations and employing neural networks to approximate sampling distributions. These advancements are crucial for enhancing the reliability of statistical inference across diverse fields, from improving machine learning model interpretability and conformal prediction to enabling more precise hypothesis testing in areas like natural language processing and GPS fault detection. The development of efficient and accurate methods for handling test statistics directly impacts the validity and power of statistical analyses.