▁sample ▁X n . ▁Then , ▁H n ( • )   =   ξ n − 1 ( • ) ▁is ▁a ▁confidence ▁distribution ▁for   θ . ▁ ▁E fr on ▁stated ▁that ▁this ▁distribution ▁" assign s ▁probability ▁ 0 . 0 5 ▁to ▁ θ ▁lying ▁between ▁the ▁upper ▁end points ▁of ▁the ▁ 0 . 9 0 ▁and ▁ 0 . 9 5 ▁confidence ▁interval , ▁etc ." ▁and ▁" it ▁has ▁powerful ▁intuit ive ▁appeal ". ▁ ▁In ▁the ▁classical ▁literature , ▁the ▁confidence ▁distribution ▁function ▁is ▁interpreted ▁as ▁a ▁distribution ▁function ▁of ▁the ▁parameter ▁ θ , ▁which ▁is ▁impossible ▁unless ▁fid u cial ▁reasoning ▁is ▁involved ▁since , ▁in ▁a ▁frequent ist ▁setting , ▁the ▁parameters ▁are ▁fixed ▁and ▁non random . ▁ ▁To ▁interpret ▁the ▁CD ▁function ▁entirely ▁from ▁a ▁frequent ist ▁view point ▁and ▁not ▁interpret ▁it ▁as ▁a ▁distribution ▁function ▁of ▁a ▁( fixed / non random ) ▁parameter ▁is ▁one ▁of ▁the ▁major ▁depart ures ▁of ▁recent ▁development ▁relative ▁to ▁the ▁classical ▁approach . ▁The ▁nice ▁thing ▁about ▁tre ating ▁confidence ▁distributions ▁as ▁a ▁purely ▁frequent ist ▁concept ▁( similar ▁to ▁a ▁point ▁estim ator ) ▁is ▁that ▁it ▁is ▁now ▁free ▁from ▁those ▁restrict ive , ▁if ▁not ▁controvers ial , ▁constraints ▁set ▁forth ▁by ▁Fish er ▁on ▁fid u cial ▁distributions . ▁ ▁The ▁modern ▁definition ▁▁ ▁The ▁following ▁definition ▁applies ; ▁ Θ ▁is ▁the ▁parameter ▁space ▁of ▁the ▁unknown ▁parameter ▁of ▁interest ▁ θ , ▁and ▁