Experimental design and statistical data analyses are fundamental components of animal science research. Proper design of experiments and adequate sampling permits testing hypotheses raised by researchers and sets the stage for collecting required data and subsequent statistical analysis. When designing experiments, researchers should respect rules of randomization of treatments to avoid statistical bias and permit proper inference to be drawn. Use of sample sizes that result in adequate statistical power to identify the hypothesized differences among factor levels of interest is key and should be driven by formal processes determining such. Best practices for data collection should be performed to obtain high quality data by reducing collection (e.g., mislabeling, improper technique) and measurement errors. With sound data, appropriate and optimal statistical methods should be used to generate valid results. The statistical method deployed should be chosen based on assumptions about residuals (e.g., normality, correlation, and homogeneity) and on the type of data (e.g., quantitative continuous or categorical). The appropriate statistical model used should also be consistent with the experimental design to validate the respective test statistics. The science of statistics is changing rapidly. With the development of high-throughput technologies, the generation of large datasets, high performance and sophisticated models and the interest in Big Data, the training of animal science graduate students in data management and rigorous statistical analyses is more important than ever. In order to meet the demands of current trends, animal science graduate students must be trained in several complex statistical and computational skills to meet the challenges imposed by these complicated, sophisticated and nuanced analytical methods. The livestock production sector will benefit from improved training, use of advanced and appropriate experimental designs, and collection and analysis of quality data in research.

中文翻译：

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189 统计学基础知识、常见错误和研究生教育

实验设计和统计数据分析是动物科学研究的基本组成部分。适当的实验设计和充足的抽样可以检验研究人员提出的假设，并为收集所需数据和随后的统计分析奠定基础。在设计实验时，研究人员应尊重治疗随机化的规则，以避免统计偏差并允许进行适当的推断。使用具有足够统计能力的样本量来识别感兴趣的因素水平之间的假设差异是关键，并且应该由确定此类的正式流程驱动。应执行数据收集的最佳实践，以通过减少收集（例如，错误标记、不正确的技术）和测量错误来获得高质量的数据。有了声音数据，应使用适当和最佳的统计方法来产生有效的结果。应根据残差假设（例如，正态性、相关性和同质性）和数据类型（例如，定量连续或分类）来选择所部署的统计方法。使用的适当统计模型也应与实验设计一致，以验证各自的测试统计数据。统计科学正在迅速变化。随着高通量技术的发展、大数据集、高性能和复杂模型的产生以及对大数据的兴趣，动物科学研究生在数据管理和严格统计分析方面的培训比以往任何时候都更加重要。为顺应当下潮流的需求，动物科学研究生必须接受数种复杂统计和计算技能的培训，以应对这些复杂、精密和细致入微的分析方法带来的挑战。畜牧生产部门将受益于改进的培训、先进和适当的实验设计的使用以及研究中质量数据的收集和分析。