Definitions

Information bias

Information bias

Information bias is a type of cognitive bias. Information bias occurs due to people’s curiosity and confusion of goals when trying to choose a course of action. This notion is different from the objective notion of information bias in epidemiology, etc.: there the term means bias induced by non-comparable information sources, or, in other words, bias due to an object of a study influencing the nature, precision and completeness of the study data collected about that object (example: a distorted and imprecise case history is obtained from those with incipient demential memory loss, potentially biasing the search for external causes of dementia).

People often have a bias that the more information one can acquire to make a decision, the better. But often, extra information cannot affect our decision – what is not worth knowing is not worth knowing.

Examples of information bias are prevalent in medical diagnosis. Subjects in experiments concerning medical diagnostic problems show a bias in which they seek information that is unnecessary in deciding the course of treatment.

In an experiment conducted by Baron, Beattie, and Hershey (1988), subjects considered this diagnostic problem involving fictitious diseases: A patient’s presenting symptoms and history suggest a diagnosis of globoma, with about .8 probability. If it isn’t globoma, it’s either popitis or flapemia. Each disease has its own treatment, which is ineffective against the other two diseases. A test called the ET scan would certainly yield a positive result if the patient had popitis, and a negative result if she has flapemia. If the patient has globoma, a positive and negative result are equally likely. If the ET scan was the only test you could do, should you do it? Why or why not?

Many subjects answered that they would conduct the ET scan even if it were costly, and even if it were the only test that could be done. However, the test in question does not affect the course of action as to what treatment should be done. Because the probability of globoma is so high with a probability of .8, the patient would be treated for globoma no matter what the test says. Globoma is the most probable disease before or after the ET scan.

In this example, we can calculate the value of the ET scan. Out of 100 patients, a total of 80 people will have globoma regardless of whether the ET scan is positive or negative. Since it is equally likely for a patient with globoma to have a positive or negative ET scan result, 40 people will have a positive ET scan and 40 people will have a negative ET scan, which totals to 80 people having globoma. This means that a total of 20 people will have either popitis or flapemia regardless of the result of the ET scan. The number of patients with globoma will always be greater than the number of patients with popitis or flapemia in either case of a positive or negative ET scan so the ET scan is useless in determining what disease to treat. The ET scan will indicate that globoma should be treated regardless of the result.

Calculating the value of information will help avoid information bias. Information that is not worth knowing is not worth acquiring.

Baron, J. (1988, 1994, 2000). Thinking and Deciding. Cambridge University Press.

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