P Value

P-values are statistical measures indicating the probability of observing data as extreme as, or more extreme than, the obtained results, assuming a null hypothesis is true. Current research focuses on improving p-value accuracy and reliability across diverse applications, including anomaly detection (using conformal prediction methods) and hypothesis testing in high-dimensional data (e.g., through adaptations of the Dip test and novel multivariate unimodality tests). These advancements aim to enhance the rigor and interpretability of statistical inference, particularly in machine learning and complex data analysis, leading to more reliable conclusions and improved decision-making in various scientific fields. Furthermore, research addresses challenges like controlling false discovery rates in multiple testing scenarios and developing computationally efficient methods for p-value calculation in complex models.