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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]
Prediction intervals are commonly used as definitions of reference ranges, such as reference ranges for blood tests to give an idea of whether a blood test is normal or not. For this purpose, the most commonly used prediction interval is the 95% prediction interval, and a reference range based on it can be called a standard reference range.
If the region does comprise an interval, then it is called a likelihood interval. [16] [18] [22] Likelihood intervals, and more generally likelihood regions, are used for interval estimation within likelihoodist statistics: they are similar to confidence intervals in frequentist statistics and credible intervals in Bayesian statistics.
In summary, a set of the real numbers is an interval, if and only if it is an open interval, a closed interval, or a half-open interval. [4] [5] A degenerate interval is any set consisting of a single real number (i.e., an interval of the form [a, a]). [6] Some authors include the empty set in this definition.
The blue intervals contain the population mean, and the red ones do not. This probability distribution highlights some different confidence intervals. Informally, in frequentist statistics, a confidence interval (CI) is an interval which is expected to typically contain the parameter being estimated.
Estimation statistics, or simply estimation, is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. [1]
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The construction of binomial confidence intervals is a classic example where coverage probabilities rarely equal nominal levels. [3] [4] [5] For the binomial case, several techniques for constructing intervals have been created. The Wilson score interval is one well-known construction based on the normal distribution. Other constructions ...