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Life testing observes failure times under a specified test protocol to estimate a lifetime probability distribution or check a reliability theory model. A test can end before every unit fails. Under right censoring, one records $Y_i=\min (T_i,C_i)$ and ${\delta }_i=1_{\{T_i\le C_i\}}$, where $T_i$ is the failure time and $C_i$ is the observation limit.

For independent censoring, the part of the likelihood function involving a continuous lifetime model with probability density function $f$ and survival function $R$ isA unit that fails at 45 hours contributes $f(45)$; one still working when observation ends at 60 hours contributes $R(60)$. Treating the latter as a failure at 60 hours invents an event that was not observed. The test protocol and any dependence between failure and observation limits therefore affect what can be inferred.^[ref]

Philip J. Boland works on statistical and probabilistic aspects of reliability and the history of statistics. UCD's 2005–06 institutional record lists research interests in reliability theory, life testing, actuarial probability and mathematical statistics. These are historical affiliations and interests in that record, rather than a claim about his present appointment.^[ref]

reliability theory connects distributions of component lifetimes to the behavior of a whole system. Statistical inference then estimates those lifetime distributions or compares designs from observed failures and surviving units. Boland also wrote the Dictionary of Irish Biography entry on William Sealy Gosset, connecting the mathematical development of small-sample inference with its industrial setting.^[ref]

Institutional biographical record: www.ucd.ie/students/t4media/2005-6%20science_pg.pdf