Robin Hanson writes,

let me outline an argument for the importance of overcoming bias:

1. Our beliefs have many errors, i.e., deviations from truth.
2. Reducing error is important goal, for which we are willing to pay substantial costs.
3. The causes of our errors can be seen as ranging from context specific to general trends.
4. We in fact have many identifiable stable general error trends, in addition to legion context specific causes.
5. By reflecting on error causes, we can seek ways to adjust our pattens of thought and social institutions to reduce error.
6. For a substantial fraction of error causes, we can in fact find feasible adjustments.
7. It is often more cost-effective to seek and implement adjustments for general trends, than for context specific errors.

I think that there are many strategies for finding truth. A biased trial-and-error strategy that makes lots of trials may be better than a strategy that focuses on bias but makes few trials. The only bias that really worries me is confirmation bias.

Think of seeking truth as like one of those parlor guessing games where you make a guess and then get feedback of the form “You’re getting warmer” or “You’re getting colder.”

Suppose you and I each get half an hour of guesses and feedback. If I make lots of biased guesses and you make one unbiased guess, then at the end of half an hour I may be a lot closer than you are to the right answer, even though where I am is conditional on my initial biases.

The one bias that really causes trouble is confirmation bias. Confirmation bias means that when I get feedback that says, “You’re getting colder,” I interpret it as saying “You’re getting warmer.” In that case, additional guesses and feedback may not help me.

I have nothing against trying to overcome other types of biases. But for me, the most important bias to worry about is confirmation bias. That is the one that merits eternal vigilance.

To use statistical jargon, I believe that as long as I do not suffer from confirmation bias, then a process of guessing and then obtaining feedback will asymptotically converge to the truth. It may not be as efficient as a process that is free of other biases, but the cost of getting rid of the other biases may not necessarily exceed the benefits.