Confirmation bias (also known as positive bias) is the tendency to search for, interpret, favor, and recall information in a way that confirms or strengthens one'one's prior personal beliefs or hypotheses [1].   For example, one might test hypotheses with positive rather than negative examples, thus missing obvious disconfirming tests.

“I had, also, during many years followed a golden rule, namely, that whenever a published fact, a new observation or thought came across me, which was opposed to my general results, to make a memorandum of it without fail and at once; for I had found by experience that such facts and thoughts were far more apt to escape from the memory than favourable ones. Owing to this habit, very few objections were raised against my views which I had not at least noticed and attempted to answer.” - Charles Darwin (autobiography)

See also: Motivated skepticism, Privileging the hypothesis, Falsifiability, Heuristics and Biases, Availability heuristic, Surprise, Narrative fallacy, Privileging the hypothesis, Heuristics and Biases

Confirmation bias (also known as positive bias) is the tendency to search for, interpret, favor, and recall information in a way that confirms or strengthens one'one's prior personal beliefs or hypotheses [1].   For example, one might test hypotheses with positive rather than negative examples, thus missing obvious disconfirming tests.

See also: Motivated skepticism, Availability biasheuristic, Surprise, Narrative fallacy, Privileging the hypothesis, Heuristics and Biases

See also: Motivated skepticism, Availability bias, Surprise, Narrative fallacy, Privileging the hypothesis, Heuristics and Biases

Confirmation bias (also known as positive bias) is the tendency to search for, interpret, favor, and recall information in a way that confirms or strengthens one's prior personal beliefs or hypotheses [1].  For example, one might test hypotheses with positive rather than negative examples, thus missing obvious disconfirming tests.

External Links

• Speculations on the Future of Science by Kevin Kelly
• On the Failure to Eliminate Hypotheses in a Conceptual Task by P.C. Wason
• Write Your Hypothetical Apostasy by Nick Bostrom
• Confirmation Bias, Wikipedia

Positive or confirmationConfirmation bias is athe tendency to testsearch for, interpret, favor, and recall information in a way that confirms or strengthens one's prior personal beliefs or hypotheses with positive rather than negative examples, thus risking to miss obvious disconfirming tests.

Kevin Kelly argues that negative results should be "saved, shared, compiled and analyzed, instead of being dumped. Positive results may increase their credibility when linked to negative results." [1 If so then this bias is particularly dangerous as the lack of negative results would themselves cast doubt on even entirely valid conclusions.

As an extreme example, imagine one hundred algorithms for stock market prediction placed in one hundred safety deposit boxes under a hundred different assumed names. Ten years later, to great fanfare, only one box, the one containing the most accurate post-facto results, is opened. There is no universally accepted and undeniable way to prove that any of the other 99 did or didn't exist without a discipline that forces negative result reporting. Even a person who had filed away only one algorithm under their own name, once, would therefore be suspect.

Blog posts

• Positive Bias: Look Into the Dark
• Positive Bias Test (C++ program) by MBlume

External links

• Online test implementing Wason's experiment]. (this link is currently not working, as of Jan. 19, 2012)
• online version of MBlume's C++ program
• 2 Kevin Kelly on future science

References

• (PDF)

See also

• Motivated skepticism, Availability bias
• Surprise
• Narrative fallacy, Privileging the hypothesis
• Write Your Hypothetical Apostasy

• Motivated skepticism, Availability bias
• Surprise
• Narrative fallacy, Privileging the hypothesis
• Write Your Hypothetical Apostasy

Kevin Kelly argues that negative results should be "saved, shared, compiled and analyzed, instead of being dumped. Positive results may increase their credibility when linked to negative results." 1 If so then this bias is particularly dangerous as the lack of negative results would themselves cast doubt on even entirely valid conclusions.

As an extreme example, imagine one hundred algorithms for stock market prediction placed in one hundred safety deposit boxes under a hundred different assumed names. Ten years later, to great fanfare, only one box, the one containing the most accurate post-facto results, is opened. There is no universally accepted and undeniable way to prove that any of the other 99 did or didn't exist without a discipline that forces negative result reporting. Even a person who had filed away only one algorithm under their own name, once, would therefore be suspect.

• Online test implementing Wason's experiment. (this link is currently not working, as of Jan. 19, 2012)
• online version of MBlume's C++ program
• 2 Kevin Kelly on future science