All economists are familiar with the problem of selection bias.  In non-randomized samples, patients may choose to be in either the treatment or control group based on factors which are also related to the outcome of interest.  Even if researchers can design a study that fully controls for selection bias, robust studies must also account for other biases.  These include:

  • Recall bias: Patients in one group have better or worse memory of a given event.  If one wishes to compare changes in income for individual who received certain workforce training, individuals who participated in the program may be more or less likely to inflate their income levels over time.
  • Interviewer bias: If new data is being collected and researchers use separate interviewers for the treatment and control groups, if one interviewer systematically over/understates the interviewee responses, the study results will be biased.
  • Observation bias: This problem is particularly problematic for medical studies.  Observation bias occurs when physicians (or patients) are more likely to detect a disease.  Thus, a study identifying how pollution affected disease rates may underestimate the impact of the pollution if those affected are less likely to detect any disease than those who are not.  For instance, if poor individuals are more likely to drink polluted water than rich individuals, but also less likely to go to the doctor, the disease incidence from polluted water would be underreported and the causal impact of water pollution would be underestimated.

Outside of purely statistical biases, the research community at large may suffer from other biases as well.  These include:

  • Funding bias: Researcher bias towards interpreting quantitative results in favor of the entity which funded their study.
  • Status quo bias: Survey respondents may base their opinions closer to the status quo or researchers can interpret their results in a fashion more likely to coincide with the existing academic literature.
  • Publication Bias: tendency of researchers, editors, and pharmaceutical companies to handle the reporting of experimental results that are positive (i.e. showing a significant finding) differently from results that are negative (i.e. supporting the null hypothesis) or inconclusive, leading to bias in the overall published literature.
  • Hindsight bias: is the inclination to see events that have already occurred as being more predictable than they were before they took place


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