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.

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, fiducial intervals, tolerance intervals, and prediction intervals. For a non-statistical method, interval estimates can be deduced from fuzzy logic.

Caution Using and Building Estimates

See also: Tolerance interval § Relation to other intervals, Confidence interval § Alternatives and critiques, and Prediction interval § Contrast with parametric confidence intervals

When determining the significance of a parameter, it is best to understand the data and its collection methods. Before collecting data, an experiment should be planned such that the uncertainty of the data is sample variability, as opposed to a statistical bias. After experimenting, a typical first step in creating interval estimates is plotting using various graphical methods. From this, one can determine the distribution of samples from the data set. Producing interval boundaries with incorrect assumptions based on distribution makes a prediction faulty.

When interval estimates are reported, they should have a commonly held interpretation within and beyond the scientific community. In this regard, credible intervals are held to be most readily understood by the general public^{[citation needed]}. Interval estimates derived from fuzzy logic have much more application-specific meanings.

In 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. However, in more novel situations there should be guidance on how interval estimates can be formulated. In this regard confidence intervals and credible intervals have a similar standing but there two differences. First, credible intervals can readily deal with prior information, while confidence intervals cannot. Secondly, confidence intervals are more flexible and can be used practically in more situations than credible intervals: one area where credible intervals suffer in comparison is in dealing with non-parametric models.

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 be 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.

Severini discusses conditions under which credible intervals and confidence intervals will produce similar results, and also discusses both the coverage probabilities of credible intervals and the posterior probabilities associated with confidence intervals.

In decision theory, which is a common approach to and justification for Bayesian statistics, interval estimation is not of direct interest. The outcome is a decision, not an interval estimate, and thus Bayesian decision theorists use a Bayes action: they minimize expected loss of a loss function with respect to the entire posterior distribution, not a specific interval.

See also

• 68–95–99.7 rule
• Algorithmic inference
• Coverage probability
• Estimation statistics
• Induction (philosophy)
• Margin of error
• Multiple comparisons
• Philosophy of statistics
• Predictive inference
• Behrens–Fisher problem This has played an important role in the development of the theory behind applicable statistical methodologies.

Bibliography

• 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.

• Meeker, W.Q., Hahn, G.J. and Escobar, L.A. (2017). Statistical Intervals: A Guide for Practitioners and Researchers (2nd Edition). John Wiley & Sons.