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134 bytes added ,  03:25, August 21, 2025
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{{math-h}}
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'''Standard deviation''' is a measure in [[statistics]] of the [[dispersion]] of a set of values (represented as <math>X</math>).  It is defined as the square root of the [[variance:Probability and Statistics|variance]] of these values, where variance is defined as
 
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'''Standard deviation''' is a measure in [[Statistics|statistics]] of the [[dispersion]] of a set of values (represented as <math>X</math>).  It is defined as the square root of the [[variance]] of these values, where variance is defined as
      
:<math>\sigma^2 = \operatorname{E}[(X-\operatorname{E}[X])^2] = \operatorname{E}[X^2] - (\operatorname{E}[X])^2</math>
 
:<math>\sigma^2 = \operatorname{E}[(X-\operatorname{E}[X])^2] = \operatorname{E}[X^2] - (\operatorname{E}[X])^2</math>
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The formula for standard deviation must not be confused with the formula
 
The formula for standard deviation must not be confused with the formula
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:<math>S_{n} = \sqrt {\sum_n(x - \bar x)^2 \over n - 1}</math>
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:<math>S_{n} = \sqrt {\sum_n(X_n - \bar X)^2 \over n - 1}</math>
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which is the formula for a [[point estimate]] of the true standard deviation from a sample size of ''n''.  As such this estimator itself has a variance which, as the formula indicates, decreases as the sample size increases.
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(where <math>\bar X =  {\sum_n X_n  \over N}</math> is the [[sample mean]]).
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which is the formula for a [[point estimate]] of the true standard deviation from a sample size of ''n''.  As such this [[statistical estimator]] itself has a variance which, as the formula indicates, decreases as the sample size increases.
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{{math-h}}
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[[category:Probability and Statistics]]
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[[Category:Probability and Statistics]]
 
[[Category:Mathematics]]
 
[[Category:Mathematics]]
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