As many have pointed out, there is nothing new or nothing surprising in the claim. What the statistical theories claim is that if a variable is truly (mathematically) random the statistical distribution asymptotes to a Gaussian distribution (or the Bell curve). That's not an observation or a fact. That's a theorem which one can prove, in other words, it's more like a definition of a "true randomness" of a variable. Roughly speaking, if something is truly random, its distribution will begin to look like a Bell curve. The real question is, "what is truly random?"
It's almost nonsensical to state that the nature does not follow the Gaussian curve just because a statistical variable does not follow it. Perhaps it tells you more about the variable itself. If a variable x has a perfect Gaussian distribution, the distribution of log(x) will look nothing like a Gaussian distribution. Does that tell us the Gaussian curve is not the normal curve? It only tells us that even if x is truly random log(x) is not.
Slashdot Mirror
As many have pointed out, there is nothing new or nothing surprising in the claim. What the statistical theories claim is that if a variable is truly (mathematically) random the statistical distribution asymptotes to a Gaussian distribution (or the Bell curve). That's not an observation or a fact. That's a theorem which one can prove, in other words, it's more like a definition of a "true randomness" of a variable. Roughly speaking, if something is truly random, its distribution will begin to look like a Bell curve. The real question is, "what is truly random?"
It's almost nonsensical to state that the nature does not follow the Gaussian curve just because a statistical variable does not follow it. Perhaps it tells you more about the variable itself. If a variable x has a perfect Gaussian distribution, the distribution of log(x) will look nothing like a Gaussian distribution. Does that tell us the Gaussian curve is not the normal curve? It only tells us that even if x is truly random log(x) is not.