Interval estimation

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.^[1]

The most prevalent forms of interval estimation are confidence intervals (a frequentist method) and credible intervals (a Bayesian method).^[2] Less common forms include likelihood intervals, fiducial intervals, tolerance intervals, and prediction intervals. For a non-statistical method, interval estimates can be deduced from fuzzy logic.

^ Neyman, J. (1937). "Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability". Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences. 236 (767). The Royal Society: 333–380. Bibcode:1937RSPTA.236..333N. doi:10.1098/rsta.1937.0005. ISSN 0080-4614. JSTOR 91337. S2CID 19584450. Retrieved 2021-07-15.
^ Severini, Thomas A. (1991). "On the Relationship between Bayesian and Non-Bayesian Interval Estimates". Journal of the Royal Statistical Society, Series B (Methodological). 53 (3). Wiley: 611–618. doi:10.1111/j.2517-6161.1991.tb01849.x. ISSN 0035-9246.