= Solution
A <censoring> observation means an event time is not observed exactly: the information supplies a bound or interval instead. Under <right censoring>, one observes $X=\min(T,C)$ and the event indicator $\delta=\mathbf1_{\{T\le C\}}$; if $\delta=0$, one knows only $T>C$. Left <censoring> gives an upper bound, while interval <censoring> locates the event between two observations.
<Uninformative censoring> means that, conditional on the covariates used in the analysis, the <censoring> mechanism supplies no additional information about the event time. <Independence> $T\perp C$ within those covariate strata is a sufficient formulation: subjects remaining in the observed <risk set> have the same future <hazard> as the corresponding event-free population. A fixed study cutoff unrelated to prognosis is an example of <administrative censoring>.
<Informative censoring> occurs when that condition fails. For example, students at high risk of leaving may also become harder to contact before their departure is formally recorded; <censoring> at loss of contact selectively removes high-risk students. Ordinary <Kaplan–Meier estimator> and <Nelson–Aalen estimator> calculations then use an unrepresentative <risk set>. \b[The distinction determines whether ordinary survival estimates can be interpreted as population survival.] Merely knowing that an observation is censored does not establish <independence>.
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