Hypothesis testing requires meaningful ways to quantify biological phenomena and account for alternative mechanisms that could explain the same pattern. Researchers combine experiments, statistics, and indices to account for these confounding mechanisms. Key concepts in ecology and evolution, such as niche breadth (NB) or fitness, can be represented by several indices, which often provide uncorrelated estimates. Is this because the indices use different types of noisy data or because the targeted phenomenon is complex and multidimensional? We discuss implications of these scenarios and propose five steps to aid researchers in identifying and combining indices, experiments, and statistics. Building on prior efforts to construct databases of hypotheses and indices and document assumptions, these steps help provide a formal strategy to reduce self-confirmatory bias.

假设检验需要有意义的方法来量化生物现象并解释可以解释相同模式的替代机制。研究人员结合实验、统计数据和指数来解释这些混杂机制。生态学和进化中的关键概念，例如生态位宽度 (NB) 或适应性，可以用多个指数来表示，这些指数通常提供不相关的估计。这是因为指数使用不同类型的噪声数据还是因为目标现象是复杂且多维的？我们讨论了这些场景的影响，并提出了五个步骤来帮助研究人员识别和组合指数、实验和统计数据。基于之前构建假设和指数以及记录假设数据库的努力，这些步骤有助于提供正式的策略来减少自我证实偏差。