Evidence of Experimental 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.Most experiments should ideally be conducted "blind," to avoid introducing bias. A survey of thousands of studies reveals stronger effect sizes and more significant p-values in nonblind papers, suggesting that blinding should not be neglected.