Which is More Useful: Empirical Data or Human Judgment?

Let's say you're a physician and you prescribe a medication to a patient. This medication has known side effects, but the patient reports a side-effect that seems impossible to ascribe to the medication.

Which would you initially feel more comfortable believing:

A) The patient is reporting a symptom unrelated to the medication (your bias: empirical data), or

B) The patient is suffering from a heretofore-unknown side effect (your bias: human judgment).

Now let's say that you just charted a driving route with a mapping program, and a person familiar with the area sees the computer-generated route, and says, "I drive around there all the time and know of a faster route."

Which would you initially feel more comfortable believing:

A) The program, using mathematical algorithms free of human bias, is correct (your bias: empirical data), or

B) The person, with knowledge that the computer doesn't have, is correct (your bias: human judgment).

In both cases, the empirical data derived from the agglomeration of large quantities of data and objective measurements is free of human biases; e.g., the patient might be a hypochondriac, and your motoring friend might be avoiding the best route because of one or two bad experiences.

But...in both cases, the empirical data was also generated by humans -- humans who can easily overlook critical factors when assembling data. And the empirical data was processed by a quick-calculating, but nevertheless very dumb, piece of electronic equipment that cannot consider any factors beyond what humans fed it.

So, the answer is: There is no simple answer; just consider the quality of your sources.