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Error resides on the statistical side of the fault line separating the deductive tools of mathematics from the inductive tools of statistics. On the mathematics side of the chasm lays perfect information, and on the statistics side exists estimation in the face of uncertainty. For the purposes of estimation, error describes the unknown, provides a basis for comparison, and serves as a hypothesized placeholder enabling estimation. This entry discusses the role of error from a modeling perspective and in the context of regression, ordinary least squares estimation, systematic error, random error, error distributions, experimentation, measurement error, rounding error, sampling error, and nonsampling error.

Modeling

For practical purposes, the universe is stochastic. For example, any “true” model involving gravity would require, at least, a parameter for every particle ...

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