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Challenges in statistical inference for large operational experiments

机译：大型操作实验中统计推断的挑战

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摘要

Operationally scaled silviculture experiments are typically multi-disciplinary. Outcome priorities are typically based on criteria that differ among disciplines. If precise, unbiased estimates of effects and an ability to infer results to units similar to the ones in the study are important, the objectives can be prioritized into primary statistical objectives that drive the study design and secondary statistical objectives that can be met within the structure imposed by the primary objectives. The design phase of a study provides an opportunity to assess how various choices related to replication, randomization and sampling affect precision, bias and statistical inference. The use and role of statistical hypothesis testing to address objectives should also be evaluated. Throughout the study design process and the implementation of the study, coordination and communication among disciplines is important. Examples are provided.
机译：可操作规模的造林实验通常是多学科的。成果优先级通常基于不同学科的标准。如果对效果进行精确，无偏的估计以及将结果推断为与研究中相似的单位的能力很重要，则可以将目标优先划分为主要的统计目标，以驱动研究设计，并在结构内实现次要的统计目标由主要目标强加。研究的设计阶段提供了一个机会，可以评估与复制，随机化和抽样相关的各种选择如何影响精度，偏差和统计推断。还应评估统计假设检验在解决目标中的用途和作用。在整个研究设计过程和研究的实施过程中，各学科之间的协调与沟通非常重要。提供示例。

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