Early warning signals and the prosecutor's fallacy
Early warning signals and the prosecutor's fallacy
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DOI：
10.1098/rspb.2012.2085
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发表时间：
2012-12-07
影响因子：
4.7
通讯作者：
Hastings, Alan
中科院分区：
文献类型：
--
作者：
Boettiger, Carl;Hastings, Alan
已经提出了预警信号，以预测发生关键过渡的可能性，例如湖泊富营养化、珊瑚礁坍塌或冰期结束。由于这样的过渡通常在时间和空间尺度上展开，而实验操作可能很难接近，因此研究经常依赖于历史观察作为自然实验的来源。在这里，我们考察了基于我们已经观察到关键过渡的事实来选择要研究的系统和那些我们希望预测过渡方法的系统之间的关键区别。这种差异的产生是因为有条件地选择已知经历了某种过渡的系统，而没有考虑到这带来的偏见--这是一种通常被称为检察官谬误的统计错误。通过分析纯粹偶然经历转换的模拟系统，我们发现在常见的警告信号统计中，假阳性率上升。我们进一步展示了一种基于模型的方法，它比那些更常用的汇总统计数据更不容易受到这种偏见的影响。我们注意到，带有重复的实验研究完全避免了这一陷阱。
Early warning signals have been proposed to forecast the possibility of a critical transition, such as the eutrophication of a lake, the collapse of a coral reef or the end of a glacial period. Because such transitions often unfold on temporal and spatial scales that can be difficult to approach by experimental manipulation, research has often relied on historical observations as a source of natural experiments. Here, we examine a critical difference between selecting systems for study based on the fact that we have observed a critical transition and those systems for which we wish to forecast the approach of a transition. This difference arises by conditionally selecting systems known to experience a transition of some sort and failing to account for the bias this introduces-a statistical error often known as the prosecutor's fallacy. By analysing simulated systems that have experienced transitions purely by chance, we reveal an elevated rate of false-positives in common warning signal statistics. We further demonstrate a model-based approach that is less subject to this bias than those more commonly used summary statistics. We note that experimental studies with replicates avoid this pitfall entirely.