Experimenter's Bias - Classification of Experimenter's Biases

Classification of Experimenter's Biases

Modern electronic or computerized data acquisition techniques have greatly reduced the likelihood of such bias, but it can still be introduced by a poorly designed analysis technique. Experimenter's bias was not well recognized until the 1950s and 60's, and then it was primarily in medical experiments and studies. Sackett (1979) catalogued 56 biases that can arise in sampling and measurement in clinical research, among the above-stated first six stages of research. These are as follows:

1. In reading-up the field
   1. the biases of rhetoric
   2. the all's well literature bias
   3. one-sided reference bias
   4. positive results bias
   5. hot stuff bias
2. In specifying and selecting the study sample
   1. popularity bias
   2. centripetal bias
   3. referral filter bias
   4. diagnostic access bias
   5. diagnostic suspicion bias
   6. unmasking (detection signal) bias
   7. mimicry bias
   8. previous opinion bias
   9. wrong sample size bias
   10. admission rate (Berkson) bias
   11. prevalence-incidence (Neyman) bias
   12. diagnostic vogue bias
   13. diagnostic purity bias
   14. procedure selection bias
   15. missing clinical data bias
   16. non-contemporaneous control bias
   17. starting time bias
   18. unacceptable disease bias
   19. migrator bias
   20. membership bias
   21. non-respondent bias
   22. volunteer bias
3. In executing the experimental manoeuvre (or exposure)
   1. contamination bias
   2. withdrawal bias
   3. compliance bias
   4. therapeutic personality bias
   5. bogus control bias
4. In measuring exposures and outcomes
   1. insensitive measure bias
   2. underlying cause bias (rumination bias)
   3. end-digit preference bias
   4. apprehension bias
   5. unacceptability bias
   6. obsequiousness bias
   7. expectation bias
   8. substitution game
   9. family information bias
   10. exposure suspicion bias
   11. recall bias
   12. attention bias
   13. instrument bias
5. In analyzing the data
   1. post-hoc significance bias
   2. data dredging bias (looking for the pony)
   3. scale degradation bias
   4. tidying-up bias
   5. repeated peeks bias
6. In interpreting the analysis
   1. mistaken identity bias
   2. cognitive dissonance bias
   3. magnitude bias
   4. significance bias
   5. correlation bias
   6. under-exhaustion bias

The effects of bias on experiments in the physical sciences have not always been fully recognized.