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A '''statistic''' is a function of a distributed variable.  Notionally, it is a calculation made on the basis of a set numbers typically derived as a sample from some presumed underlying [[probability distribution]], and usually used in order to estimate something about the distribution from which the sample is taken. The use of a '''statistic''' to characterize a set of observations is generally justified on the basis of its [[asymptote|asymptotic]] behavior, that is, a given '''statistic''' accurately characterizes the underlying phenomena only probabilistically (this consideration is the genesis of [[confidence interval]]s in [[classical statistics]]) and is considered to be accurate only in the limit as the number of observations increases without bounds.  It should be noted however that the use of [[confidence interval]]s is somewhat problematic since their calculations are based on certain presumptions about the nature of the underlying true distribution, which may or may not prove to be good.  [[Bayesian Probability]] does not use '''statistics'''.

A '''statistic''' is a function of a distributed variable.  Notionally, it is a calculation made on the basis of a set numbers typically derived as a sample from some presumed underlying [[probability distribution]], and usually used in order to estimate something about the distribution from which the sample is taken. The use of a '''statistic''' to characterize a set of observations is generally justified on the basis of its [[asymptote|asymptotic]] behavior, that is, a given '''statistic''' accurately characterizes the underlying phenomena only probabilistically (this consideration is the genesis of [[confidence interval]]s in [[classical statistics]]) and is considered to be accurate only in the limit as the number of observations increases without bounds.  It should be noted however that the use of [[confidence interval]]s is somewhat problematic since their calculations are based on certain presumptions about the nature of the underlying true distribution, which may or may not prove to be good.  

For example, suppose a [[random sample]] of three children is chosen from a particular class, and their heights measured as 1.42cm., 1.54cm., and 1.48cm; then the [[arithmetic mean]] of these heights is 1.48cm. We might then go on to use  this value of 1.48cm to represent the [[average]] height of a child in that class.

For example, suppose a [[random sample]] of three children is chosen from a particular class, and their heights measured as 1.42cm., 1.54cm., and 1.48cm; then the [[arithmetic mean]] of these heights is 1.48cm. We might then go on to use  this value of 1.48cm to represent the [[average]] height of a child in that class.