Editing Interval estimation (section)

==== Fiducial ====
{{main|Fiducial interval}} 
[[Fiducial inference]] utilizes a data set, carefully removes the noise and recovers a distribution estimator, Generalized Fiducial Distribution (GFD). Without the use of Bayes' Theorem, there is no assumption of a prior, much like confidence intervals.
Fiducial inference is a less common form of [[statistical inference]]. The founder, [[Ronald Fisher|R.A. Fisher]], who had been developing inverse probability methods, had his own questions about the validity of the process. While fiducial inference was developed in the early twentieth century, the late twentieth century believed that the method was inferior to the frequentist and Bayesian approaches but held an important place in historical context for statistical inference. However, modern-day approaches have generalized the fiducial interval into Generalized Fiducial Inference (GFI), which can be used to estimate discrete and continuous data sets.<ref>{{Cite journal |last=Hannig |first=Jan |last2=Iyer |first2=Hari |last3=Lai |first3=Randy C. S. |last4=Lee |first4=Thomas C. M. |date=2016-07-02 |title=Generalized Fiducial Inference: A Review and New Results |url=|journal=Journal of the American Statistical Association |language=en |volume=111 |issue=515 |pages=1346–1361 |doi=10.1080/01621459.2016.1165102 |issn=0162-1459}}</ref>