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
Famous quotes containing the words interval and/or estimation:
“I was interested to see how a pioneer lived on this side of the country. His life is in some respects more adventurous than that of his brother in the West; for he contends with winter as well as the wilderness, and there is a greater interval of time at least between him and the army which is to follow. Here immigration is a tide which may ebb when it has swept away the pines; there it is not a tide, but an inundation, and roads and other improvements come steadily rushing after.”
—Henry David Thoreau (1817–1862)
“... it would be impossible for women to stand in higher estimation than they do here. The deference that is paid to them at all times and in all places has often occasioned me as much surprise as pleasure.”
—Frances Wright (1795–1852)