FROM:
British Medical Journal 2003 (Jun 28); 326 (7404): 1453–1455 ~ FULL TEXT
Ted J Kaptchuk, MD
Assistant Professor of Medicine
Harvard Medical School, Osher Institute, 401 Park Drive, Boston, MA 02215, USA ted_kaptchuk@hms.harvard.edu
Doctors are being encouraged to improve their critical appraisal skills to make better use of medical research. But when using these skills, it is important to remember that interpretation of data is inevitably subjective and can itself result in bias.
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.
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