Interval estimation

Interval estimation

In statistics, interval estimation is the use of sample data to calculate an interval of possible (or probable) values of an unknown population parameter. The most prevalent forms of interval estimation are:

Other common approaches to interval estimation, which are encompassed by statistical theory, are:

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.


The scientific problems associated with interval estimation may be summarised as follows:

*When interval estimates are reported, they should have a commonly-held interpretation in the scientific community and more widely. In this regard, credible intervals are held to be most readily understood by the general public. Interval estimates derived from fuzzy logic have much more application-specific meanings.
*For commonly occurring situations there should be sets of standard procedures that can be used, subject to the checking and validity of any required assumptions. This applies for both confidence intervals and credible intervals.
*For more novel situations there should be guidance and how interval estimates can be formulated. In this regard confidence intervals and credible intervals have a similar standing but there are differences:
*credible intervals can readily deal with prior information, while confidence intervals cannot.
*confidence intervals are more flexible and can be used practically in more situations than credible intervals: one area where credible intervals is in dealing with non-parametric models (see non-parametric statistics).

*There should be ways of testing the performance of interval estimation procedures. This arises because many such procedures involve approximations of various kinds and there is a need to check that the actual performance of a procedure is close to what is claimed. The use of stochastic simulations makes this is straightforward in the case of confidence intervals, but it is somewhat more problematic for credible intervals where prior information needs to taken properly into account. Checking of credible intervals can be done for situations representing no-prior-information but the check involves checking the long-run frequency properties of the procedures.

See also

The Behrens–Fisher problem. This has played an important role in the development of the theory behind applicable statistical methodologies. This problem is one of the simplest to state but which is not easily solved. The task of specifying interval estimates for this problem is one where a frequentist approach fails to provide an exact solution, although some approximations are available. The Bayesian approach also fails to provide an answer that can be expressed as straightforward simple formulae, but modern computational methods of Bayesian analysis do allow essentially exact solutions to be found. Thus study of the problem can be used to elucidate the differences between the frequentist and Bayesian approaches to interval estimation.


  • Kendall, M.G. and Stuart, A. (1973). The Advanced Theory of Statistics. Vol 2: Inference and Relationship (3rd Edition). Griffin, London.

In the above Chapter 20 covers confidence intervals, while Chapter 21 covers fiducial intervals and Bayesian intervals and has discussion comparing the three approaches. Note that this work predates modern computationally intensive methodologies. In addition, Chapter 21 discusses the Behrens–Fisher problem.

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