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Reference.org
CDF-based nonparametric confidence interval
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<p>In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, <a href="/facts/Cumulative_distribution_function/WaKU8tp4">cumulative distribution function</a> (CDF)-based nonparametric confidence intervals are a general class of <a href="/facts/Confidence_interval/NS8mG5UE">confidence intervals</a> around <a href="/facts/V-statistic/xJRq608o">statistical functionals</a> of a distribution. To calculate these confidence intervals, all that is required is an <a href="/facts/Independent_and_identically_distributed_random_variables/othIRaWt">independently and identically distributed</a> (iid) sample from the distribution and known bounds on the support of the distribution. The latter requirement simply means that all the nonzero probability mass of the distribution must be contained in some known interval [ a , b ] {\displaystyle [a,b]} . </p>