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This paper discusses the maximum robustness approach for studying cases of adaptation in language. We live in an age where we have more data on more languages than ever before, and more data to link it with from other domains. This should make it easier to test hypotheses involving adaptation, and also to spot new patterns that might be explained by adaptation. However, there is not much discussion of the overall approach to research in this area. There are outstanding questions about how to formalize theories, what the criteria are for directing research and how to integrate results from different methods into a clear assessment of a hypothesis. This paper addresses some of those issues by suggesting an approach which is causal, incremental and robust. It illustrates the approach with reference to a recent claim that dry environments select against the use of precise contrasts in pitch. Study 1 replicates a previous analysis of the link between humidity and lexical tone with an alternative dataset and finds that it is not robust. Study 2 performs an analysis with a continuous measure of tone and finds no significant correlation. Study 3 addresses a more recent analysis of the link between humidity and vowel use and finds that it is robust, though the effect size is small and the robustness of the measurement of vowel use is low. Methodological robustness of the general theory is addressed by suggesting additional approaches including iterated learning, a historical case study, corpus studies, and studying individual speech.本论文探讨了用于研究语言适应性案例的最大化稳健性研究方法。我们所处的时代相较于以往，拥有更多语种的相关数据，同时也有更多跨领域的关联数据可供使用。这一现状本应降低适应性相关假说的检验难度，同时也便于发掘可通过适应性机制解释的新型语言模式。然而，当前学界对该领域的整体研究路径尚缺乏充分探讨，目前仍存在诸多悬而未决的议题：例如如何对理论进行形式化建模、指导研究的评判标准为何，以及如何整合不同方法的研究结果以实现对假说的清晰评估。本论文提出一种兼具因果性、增量性与稳健性的研究路径，以期解决上述部分问题。本文结合一项近期研究主张展开方法阐释：该主张认为干燥环境会抑制语言中音高精确对立的使用。研究1采用替代数据集重复验证了湿度与词汇声调之间关联的既往分析，结果显示该关联并不具备稳健性。研究2通过声调的连续量化指标开展分析，未发现显著相关关系。研究3针对湿度与元音使用关联的最新分析展开验证，结果显示该关联具备稳健性，但效应量较小，且元音使用测量的稳健性偏低。为进一步探讨通用理论的方法论稳健性，本文还提出了可补充的研究路径，包括迭代学习、历史案例研究、语料库分析以及个体言语研究。