Facts do not accumulate on the blank slates of researchers’ minds and data simply do not speak for themselves. 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.
Definitions of interpretation biases
Confirmation bias-evaluating evidence that supports one’s preconceptions differently from evidence that challenges these convictions
Rescue bias-discounting data by finding selective faults in the experiment
Auxiliary hypothesis bias-introducing ad hoc modifications to imply that an unanticipated finding would have been otherwise had the experimental conditions been different
Mechanism bias-being less sceptical when underlying science furnishes credibility for the data
“Time will tell” bias-the phenomenon that different scientists need different amounts of confirmatory evidence
Orientation bias-the possibility that the hypothesis itself introduces prejudices and errors and becomes a determinate of experimental outcomes