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In statistics, interval estimation is the use of sample data to estimate an interval of possible values of a parameter of interest. This is in contrast to point estimation, which gives a single value.
The most prevalent forms of interval estimation are confidence intervals (a frequentist method) and credible intervals (a Bayesian method); less common forms include likelihood intervals and fiducial intervals. Other forms of statistical intervals include tolerance intervals (covering a proportion of a sampled population) and prediction intervals (an estimate of a future observation, used mainly in regression analysis).
Non-statistical methods that can lead to interval estimates include fuzzy logic.