Editing Point estimation (section)

{{Short description|Parameter estimation via sample statistics}}
In [[statistics]], '''point estimation''' involves the use of [[statistical sample|sample]] [[data]] to calculate a single value (known as a '''point estimate''' since it identifies a [[Point (geometry)|point]] in some [[parameter space]]) which is to serve as a "best guess" or "best estimate" of an unknown population [[parameter]] (for example, the [[population mean]]). More formally, it is the application of a point [[estimator]] to the data to obtain a point estimate.

Point estimation can be contrasted with [[interval estimation]]: such interval estimates are typically either [[confidence interval]]s, in the case of [[frequentist inference]], or [[credible intervals]], in the case of [[Bayesian inference]]. More generally, a point estimator can be contrasted with a set estimator. Examples are given by [[Confidence region|confidence sets]] or [[Credible interval|credible sets.]] A point estimator can also be contrasted with a distribution estimator. Examples are given by [[confidence distribution]]s, [[Randomised decision rule|randomized estimators]], and [[Bayesian statistics|Bayesian posteriors]].