= 27d567bc2d48d9f40e9ccc20ea370b15)
Vaniver comments on The Power of Noise - LessWrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (80)
Eliezer claims that randomness is always bad; many other people claim that one way randomness is good is that it is unbiased. Partitioning subjects into experimental conditions must be unbiased. Using an algorithm and knowing that its biases are orthogonal to the phenomenon being investigated requires omniscience. Besides, if you knew in advance what was relevant, you wouldn't need to do the experiment.
That is what the comment means. The use of the term "AI" is just to show that the claim is that no real-world agent can be smart enough to do unbiased partitioning in all cases, not just that we're not smart enough to do it.
In practice, a possibly biased but intelligent partitioning is better when the sample size is small.
This is perhaps a useful place to illustrate the "randomness hath no power" argument: randomness is unbiased in expectation but we actually expect the absolute amount of biasedness for a randomly selected assignment to be nonzero. When biasing factors are known ahead of time, we do better by controlling for it directly (with, say, a paired assignment).