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Actually, a lot of natural phenomena does NOT follow a normal distribution. The problem is that most people assume it does anyway, and that's where a lot of er

Actually, a lot of natural phenomena does NOT follow a normal distribution. The problem is that most people assume it does anyway, and that's where a lot of errors come from. Statistics requires a lot of correct assumptions in order to be accurate; sadly, most people overlook or fail to check their assumptions.

estebank 14y ago
The thing is, natural phenomena doesn't follow normal distribution, but the sum of non-normal variables, will approach a Gaussian distribution, as per the Central Limit Theorem[1][2]. The problem is people assuming a normal distribution on a single variable with unknown distribution. [1]: http://en.wikipedia.org/wiki/Central_limit_theorem http://en.wikipedia.org/wiki/Central_limit_theorem [2]: http://stats.stackexchange.com/questions/22387/why-it-is-often-assumed-gaussian-distribution http://stats.stackexchange.com/questions/22387/why-it-is-oft...
- Xcelerate 14y ago
  Yes, that's correct. If anyone wants to know more, an elementary statistics book will teach you all about the central limit theorem and the law of large numbers. Very useful things to know.
  - mlvljr 14y ago
    Exactly, and there're also lognormal distribution and friends, which are quite hard to distinguish from normal with limited data at hand. There's no magical "naturally" normal distribution at all. And whether smth is normal depends on how and what we measure: consider a plant's leaf, whose shape is elliptical, with axes independently normally distributed; the area is not distributed normally then. :) We had quite wonderful discussions here at Russia last winter on whether the shape of some poll graphs clearly hints there was a fraud (it does not), and it turned out many people with technical education are lacking basic mathstat knowledge (but have strong "beliefs" regarding normal distribution).
  - Xcelerate 14y ago
    mlvljr, for some reason your post is showing as "dead". I'm not sure why??
  - re_todd 14y ago
    Khan Academy has a good lesson on the law of large numbers http://www.khanacademy.org/math/probability/v/law-of-large-numbers http://www.khanacademy.org/math/probability/v/law-of-large-n...

estebank 14y ago

The thing is, natural phenomena doesn't follow normal distribution, but the sum of non-normal variables, will approach a Gaussian distribution, as per the Central Limit Theorem[1][2]. The problem is people assuming a normal distribution on a single variable with unknown distribution. [1]: http://en.wikipedia.org/wiki/Central_limit_theorem http://en.wikipedia.org/wiki/Central_limit_theorem [2]: http://stats.stackexchange.com/questions/22387/why-it-is-often-assumed-gaussian-distribution http://stats.stackexchange.com/questions/22387/why-it-is-oft...

Xcelerate 14y ago
Yes, that's correct. If anyone wants to know more, an elementary statistics book will teach you all about the central limit theorem and the law of large numbers. Very useful things to know.
- mlvljr 14y ago
  Exactly, and there're also lognormal distribution and friends, which are quite hard to distinguish from normal with limited data at hand. There's no magical "naturally" normal distribution at all. And whether smth is normal depends on how and what we measure: consider a plant's leaf, whose shape is elliptical, with axes independently normally distributed; the area is not distributed normally then. :) We had quite wonderful discussions here at Russia last winter on whether the shape of some poll graphs clearly hints there was a fraud (it does not), and it turned out many people with technical education are lacking basic mathstat knowledge (but have strong "beliefs" regarding normal distribution).
- Xcelerate 14y ago
  mlvljr, for some reason your post is showing as "dead". I'm not sure why??
- re_todd 14y ago
  Khan Academy has a good lesson on the law of large numbers http://www.khanacademy.org/math/probability/v/law-of-large-numbers http://www.khanacademy.org/math/probability/v/law-of-large-n...