The central limit theorem predicts that a collection of objects in which there are no hidden
correlations (e.g. a handful of coins being tossed) will produce fluctuations which have an
approximate Gaussian distribution. By contrast, the fluctuations emerging from systems
containing correlations which cross multiple length and/or time scales can exhibit significant
deviations from Gaussian behavior.
The central limit theorem predicts that a collection of objects in which there are no hidden
correlations (e.g. a handful of coins being tossed) will produce fluctuations which have an
approximate Gaussian distribution. By contrast, the fluctuations emerging from systems
containing correlations which cross multiple length and/or time scales can exhibit significant
deviations from Gaussian behavior.
The central limit theorem predicts that a collection of objects in which there are no hidden
correlations (e.g. a handful of coins being tossed) will produce fluctuations which have an
approximate Gaussian distribution. By contrast, the fluctuations emerging from systems
containing correlations which cross multiple length and/or time scales can exhibit significant
deviations from Gaussian behavior.
The central limit theorem predicts that a collection of objects in which there are no hidden
correlations (e.g. a handful of coins being tossed) will produce fluctuations which have an
approximate Gaussian distribution. By contrast, the fluctuations emerging from systems
containing correlations which cross multiple length and/or time scales can exhibit significant
deviations from Gaussian behavior.
http://webcache.googleusercontent.com/search?q=cache:4z0wka23J00J:arxiv.org/pdf/1011.6398+%22Macroscopic+Quantum+Effect+herd+behavior%22&hl=en&gl=us
