预期的错误成本是否可以证明在多个alpha级别测试一个假设,而不是寻找一个难以捉摸的最佳alpha?
1Complexity Science, Meraglim Holdings Corporation, Palm Beach Gardens, FL, United States of America.
PloS one
|September 25, 2024
概括
同时测试一个假设在多个alpha级别提供了传统方法的替代方案. 这种方法可以导致可接受的预期总错误成本,鼓励仔细考虑错误率和证据强度.
科学领域:
- 统计 统计 统计 统计
- 统计推理 统计推理
- 假设测试 假设测试
背景情况:
- 传统的尼曼-皮尔森框架允许在单个alpha级别进行假设测试.
- 研究人员在定义最佳α水平和错误成本方面经常面临挑战.
- 了解错误率和证据的强度对于研究设计和报告至关重要.
研究的目的:
- 在Neyman-Pearson框架内引入和评估多alpha级测试方法.
- 为了证明多alpha级测试可以实现可接受的预期总错误成本.
- 为了比较多个alpha级别测试的性能与传统的单个alpha测试和优化方法.
主要方法:
- 对于单个和多个alpha级别测试,我们得出了预期错误成本的公式.
- 在二分法和连续分布中考虑了效果大小的先前概率.
- 预期的总成本在单α,多α和最佳测试策略之间进行了比较.
主要成果:
- 多个alpha级别的测试可以产生可接受的预期总错误成本.
- 优化对错误成本估计和流行假设的敏感性得到了强调.
- 与正式优化相比,在多个默认值下进行测试可以简化决策.
结论:
- 多阿尔法级别测试为单阿尔法测试和复杂的优化方法提供了切实可行的替代方案.
- 虽然不是绝对最佳的,但多阿尔法级别测试可能比依赖可能错误指定的模型的方法提供更低的平均错误成本.
- 这种方法鼓励在整个研究过程中对统计错误率和证据强度进行更细致的考虑.
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