Statistical Test

Statistical tests are crucial for evaluating the reliability and significance of results across diverse scientific domains, particularly in the context of increasingly complex data analysis pipelines and AI-driven decision-making. Current research emphasizes developing statistically sound tests for assessing the validity of results from various machine learning models (e.g., variational autoencoders, vision transformers, diffusion models) and data analysis workflows, often employing frameworks like selective inference to control error rates. This focus on rigorous statistical validation is vital for ensuring the trustworthiness of AI-driven insights in high-stakes applications such as medical diagnosis and autonomous systems, ultimately improving the reliability and reproducibility of scientific findings.