The goal of a well-controlled study is to remove unwanted variation when estimating the causal effect of the intervention of interest. Experiments conducted in the basic sciences frequently achieve this goal using experimental controls, such as "negative" and "positive" controls, which are measurements designed to detect systematic sources of unwanted variation. Here, we introduce clear, mathematically precise definitions of experimental controls using potential outcomes. Our definitions provide a unifying statistical framework for fundamental concepts of experimental design from the biological and other basic sciences. These controls are defined in terms of whether assumptions are being made about a specific treatment level, outcome, or contrast between outcomes. We discuss experimental controls as tools for researchers to wield in designing experiments and detecting potential design flaws, including using controls to diagnose unintended factors that influence the outcome of interest, assess measurement error, and identify important subpopulations. We believe that experimental controls are powerful tools for reproducible research that are possibly underutilized by statisticians, epidemiologists, and social science researchers.

翻译：控制良好的研究的目标是在估计利益干预的因果关系时消除不必要的变化。在基础科学中进行的实验经常利用实验性控制,例如“消极”和“积极”控制,来实现这一目标,这些实验性控制是用来探测不必要变化的系统来源的测量。在这里,我们引入了使用潜在结果的明确的、数学精确的实验性控制定义。我们的定义为生物和其他基础科学的实验设计基本概念提供了一个统一的统计框架。这些控制的定义是:是假设特定治疗水平、结果还是结果之间的对比。我们讨论了实验性控制,作为研究人员在设计实验和发现潜在设计缺陷时的工具,包括利用控制来诊断影响利益结果的意外因素,评估测量错误,并查明重要的亚群。我们认为实验性控制是统计人员、流行病学学家和社会科学研究人员可能利用不足的再生研究的有力工具。