Effect of interpretive bias on research evidenceBMJ 2003; 326 doi: https://doi.org/10.1136/bmj.326.7404.1453 (Published 26 June 2003) Cite this as: BMJ 2003;326:1453
- Ted J Kaptchuk (email@example.com), assistant professor of medicine1
- 1 Harvard Medical School, Osher Institute, 401 Park Drive, Boston, MA 02215, USA
- Accepted 19 March 2003
Facts do not accumulate on the blank slates of researchers' minds and data simply do not speak for themselves.1 Good science inevitably embodies a tension between the empiricism of concrete data and the rationalism of deeply held convictions. Unbiased interpretation of data is as important as performing rigorous experiments. This evaluative process is never totally objective or completely independent of scientists' convictions or theoretical apparatus. This article elaborates on an insight of Vandenbroucke, who noted that “facts and theories remain inextricably linked… At the cutting edge of scientific progress, where new ideas develop, we will never escape subjectivity.”2 Interpretation can produce sound judgments or systematic error. Only hindsight will enable us to tell which has occurred. Nevertheless, awareness of the systematic errors that can occur in evaluative processes may facilitate the self regulating forces of science and help produce reliable knowledge sooner rather than later.
Interpretative processes and biases in medical science
Science demands a critical attitude, but it is difficult to know whether you have allowed for too much or too little scepticism. Also, where is the demarcation between the background necessary for making judgments (such as theoretical commitments and previous knowledge) and the scientific goal of being objective and free of preconceptions? The interaction between data and judgment is often ignored because there is no objective measure for the subjective components of interpretation. Taxonomies of bias usually emphasise technical problems that can be fixed.3 The biases discussed below, however, may be present in the most rigorous science and are obvious only in retrospect.
Quality assessment and confirmation bias
The quality of any experimental findings must be appraised. …
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