Evidence of Experimental Bias in the Life Sciences: Why We Need Blind Data Recording

PLoS Biol. 2015 Jul 8;13(7):e1002190. doi: 10.1371/journal.pbio.1002190. eCollection 2015 Jul.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biology / standards*
  • Biology / statistics & numerical data
  • Data Collection / standards*
  • Data Collection / statistics & numerical data
  • Data Mining

Grant support

This work was funded by an Australian Research council "DECRA" fellowship to LH (DE140101481). MLH and MDJ were funded by an ARC Discovery Grant, and RL by an ARC Future Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.