> Oh yes. Our intuition is that random is fair and uniform, but it's absolutely not.
But a uniform distribution is random, isn't it? This sort of phrasing is rampant in this thread, and I'm confused by it. How can a uniform distribution be considered non-random, or "less" random than another distribution?
Randomness is about having a uniform probability of results, but that does not translate into a uniform distribution of results, nor can it, because of the de-correlation between random results. Specifically, the chance of getting the same result multiple times is non-zero, and actually can be fairly high with a lot of samples, whereas the chance of duplicate results in a uniform distribution is zero.
Randomness is about having a uniform probability of results
Well, you're still assuming the colloquial definition of "random," which implies "uniformly random." Of course, we can have a random process that is not uniformly random.
I can't tell if you're trolling or not. In case you aren't, a uniform distribution isn't "uniform" in the sense of the above quotation. You're right that it's not less random, but someone looking at draws from a uniform distribution would normally describe it as "clumpy"; hence the article.
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> Oh yes. Our intuition is that random is fair and uniform, but it's absolutely not.
But a uniform distribution is random, isn't it? This sort of phrasing is rampant in this thread, and I'm confused by it. How can a uniform distribution be considered non-random, or "less" random than another distribution?
Randomness is about having a uniform probability of results, but that does not translate into a uniform distribution of results, nor can it, because of the de-correlation between random results. Specifically, the chance of getting the same result multiple times is non-zero, and actually can be fairly high with a lot of samples, whereas the chance of duplicate results in a uniform distribution is zero.
Read the article for more details and examples.
Randomness is about having a uniform probability of results
Well, you're still assuming the colloquial definition of "random," which implies "uniformly random." Of course, we can have a random process that is not uniformly random.
I can't tell if you're trolling or not. In case you aren't, a uniform distribution isn't "uniform" in the sense of the above quotation. You're right that it's not less random, but someone looking at draws from a uniform distribution would normally describe it as "clumpy"; hence the article.
A distribution is the probability of various values before the random selection takes place. Uniformity is what we do or don't see afterward.
The results of a uniform distribution, applied randomly a large number of times are less uniform than intuition predicts.
If you had bothered to read the article, your questions would have been answered.