Suppose a large number of people each have a slightly better than 50% chance of correctly answering a yes/no question. If they answered independently, the majority would very likely be correct.

For example, suppose there are 10,000 people, each with a 51% chance of answering a question correctly. The probability that more than 5,000 people will be right is about 98%. [1]

The key assumption here is **independence**, which is not realistic in most cases. But as people move in the direction of independence, the quality of the majority vote improves. Another assumption is that people are what machine learning calls “weak learners,” i.e. that they perform slightly better than chance. This holds more often than independence, but on some subjects people tend to do worse than chance, particularly experts.

You could call this the wisdom of crowds, but it’s closer to the wisdom of markets. As James Surowiecki points out in his book The Wisdom of Crowds, crowds (as in mobs) aren’t wise; large groups of independent decision makers are wise. Markets are wiser than crowds because they aggregate more independent opinions. Markets are subject to group-think as well, but not to the same extent as mobs.

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[1] Suppose there are *N* people, each with independent probability *p* of being correct. Suppose *N* is large and *p* is near 1/2. Then the probability of a majority answering correctly is approximately

*Prob*( *Z* > (1 – 2*p*) sqrt(*N*) )

where *Z* is a standard normal random variable. You could calculate this in Python by

from scipy.stats import norm
from math import sqrt
print( norm.sf( (1 - 2*p)*sqrt(N) ) )

This post is an elaboration of something I first posted on Google+.