Data from: Prolific observer bias in the life sciences: why we need blind data recording

Observer bias and other “experimenter effects” occur when researchers’ expectations influence study outcome. These biases are strongest when researchers expect a particular result, are measuring subjective variables, and have an incentive to produce data that confirm predictions. To minimize bias, it is good practice to work “blind,” meaning that experimenters are unaware of the identity or treatment group of their subjects while conducting research. Here, using text mining and a literature review, we find evidence that blind protocols are uncommon in the life sciences and that nonblind studies tend to report higher effect sizes and more significant p-values. We discuss methods to minimize bias and urge researchers, editors, and peer reviewers to keep blind protocols in mind.观察者偏差（Observer bias）与其他“实验者效应（experimenter effects）”，指研究者的预期影响研究结果时所产生的各类偏差。当研究者预期得到特定结果、测量主观变量，且存在产出可验证预测的数据的动机时，这类偏差的影响最为显著。为尽可能降低偏差，采用“盲法（blind）”是学界公认的良好研究规范，即研究者在开展研究过程中，无法知晓研究对象的身份或所属处理组。本文通过文本挖掘与文献综述，发现生命科学领域内盲法实验方案并不常见，且非盲法研究往往会报告更大的效应量与更显著的p值。我们探讨了可用于降低偏差的相关方法，并呼吁研究者、期刊编辑与同行评审人员在工作中重视盲法实验方案的应用。