A confidence interval is a pair of statistics whose random interval satisfiesAn exact confidence interval has equality throughout. The probability refers to repeated sampling at a fixed unknown parameter; the endpoints are random before observation. It is not a posterior probability assigned to the parameter after seeing the data. Here .
The sample mean has normal distribution because the observations are independent with known variance 1. The pivotal quantity has the standard normal distribution. Consequentlyand inversion gives the confidence intervalReplacing 1.96 by the exact normal quantile gives exact 95% coverage; the printed numerical value gives the usual rounded interval. No variance estimation or Student distribution is needed.
Let and be the order statistics. For the symmetric uniform distribution, each observation is above or below with probability , independently. ThusSo is an exact 50% confidence interval.
The observed interval contains values impossible under the sampling model. Both observations must lie within one unit of the parameter, so the interval of all compatible values is . It contains the true parameter almost surely. A support-restricted confidence interval for a uniform location parameter can therefore reportMore precisely, the procedure retains exact 50% unconditional coverage, since the second interval contains almost surely. The intersection is a nonempty interval for every sample in the model and equals for these data. Reporting the full compatible interval alone is also valid and has coverage one. On this sample's large-spread event it lies wholly between the observations; this does not turn the unconditional 50% procedure into a generally 100% procedure.
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