标题： 粒子物理中统计技术的实用指南 显示英文标题

标题： A Practical Guide to Statistical Techniques in Particle Physics

摘要： 在高能物理（HEP）中，新理论的排除和发现不仅依赖于高质量实验数据的获取，还依赖于统计方法的严格应用。 这些方法提供了概率标准（如p值），用于将实验数据与理论模型进行比较，旨在尽可能准确地描述数据。 假设检验在此过程中起着核心作用，因为它能够将现有理论与对观测数据的潜在新解释进行比较。 本报告回顾了粒子物理中当前使用的关键统计方法，使用合成数据和数值比较以清晰易懂的方式说明概念。 我们的结果突显了这些统计工具在提高HEP中实验灵敏度和模型排除能力方面的实际意义。 所有数值结果均使用Python和RooFit进行估算，RooFit是一个高级统计建模包，被CERN的ATLAS和CMS合作组用于对实验数据进行建模和报告结果。 显示英文摘要

摘要： In high-energy physics (HEP), both the exclusion and discovery of new theories depend not only on the acquisition of high-quality experimental data but also on the rigorous application of statistical methods. These methods provide probabilistic criteria (such as p-values) to compare experimental data with theoretical models, aiming to describe the data as accurately as possible. Hypothesis testing plays a central role in this process, as it enables comparisons between established theories and potential new explanations for the observed data. This report reviews key statistical methods currently employed in particle physics, using synthetic data and numerical comparisons to illustrate the concepts in a clear and accessible way. Our results highlight the practical significance of these statistical tools in enhancing the experimental sensitivity and model exclusion capabilities in HEP. All numerical results are estimated using Python and RooFit, a high-level statistical modeling package used by the ATLAS and CMS collaborations at CERN to model and report results from experimental data.