In statistics, interval estimation is the use of sample data to calculate an interval of possible (or probable) values of an unknown population parameter, in contrast to point estimation, which is a single number. Neyman (1937) identified interval estimation ("estimation by interval") as distinct from point estimation ("estimation by unique estimate"). In doing so, he recognised that then-recent work quoting results in the form of an estimate plus-or-minus a standard deviation indicated that interval estimation was actually the problem statisticians really had in mind.
The most prevalent forms of interval estimation are:
- confidence intervals (a frequentist method); and
- credible intervals (a Bayesian method).
Other common approaches to interval estimation, which are encompassed by statistical theory, are:
- Tolerance intervals
- Prediction intervals - used mainly in Regression Analysis
- Likelihood intervals
There is a third approach to statistical inference, namely fiducial inference, that also considers interval estimation. Non-statistical methods that can lead to interval estimates include fuzzy logic.
An interval estimate is one type of outcome of a statistical analysis. Some other types of outcome are point estimates and decisions.
Read more about Interval Estimation: Discussion, Behrens–Fisher Problem
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