The goal of (eco-) toxicological testing is to experimentally establish a dose or concentration-response and to identify a threshold with a biologically relevant and probably non-random deviation from "normal". Statistical tests aid this process. Most statistical tests have distributional assumptions that need to be satisfied for reliable performance. Therefore, most statistical analyses used in (eco-)toxicological bioassays use subsequent pre- or assumption-tests to identify the most appropriate main test, so-called statistical decision trees. There are however several deficiencies with the approach, based on study design, type of tests used and subsequent statistical testing in general. When multiple comparisons are used to identify a non-random change against negative control, we propose to use robust testing, which can be generically applied without the need of decision trees. Visualization techniques and reference ranges also offer advantages over the current pre-testing approaches. We aim to promulgate the concepts in the (eco-) toxicological community and initiate a discussion for regulatory acceptance.

中文翻译：

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监管（生态）毒理学生物测定中统计决策树的替代方案。

（生态）毒理学测试的目标是通过实验确定剂量或浓度反应，并确定具有生物学相关性和可能与“正常”非随机偏差的阈值。统计检验有助于这一过程。大多数统计测试都有分布假设，需要满足这些假设才能获得可靠的性能。因此，（生态）毒理学生物测定中使用的大多数统计分析都使用后续的预测试或假设测试来确定最合适的主要测试，即所谓的统计决策树。然而，基于研究设计、使用的测试类型和随后的一般统计测试，该方法存在一些缺陷。当多重比较用于识别阴性对照的非随机变化时，我们建议使用稳健测试，它可以在不需要决策树的情况下普遍应用。与当前的预测试方法相比，可视化技术和参考范围也具有优势。我们的目标是在（生态）毒理学界宣传这些概念，并发起对监管接受的讨论。