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In statistics, engineering, economics, and medical research, censoring occurs when the value of a measurement or observation is only partially known.
For example, suppose a study is conducted to measure the impact of a drug on mortality rate. In such a study, it may be known that an individual's age at death is at least 75 years (but may be more). Such a situation could occur if the individual withdrew from the study at age 75, or if the individual is currently alive at the age of 75.
Censoring also occurs when a value occurs outside the range of a measuring instrument. For example, a bathroom scale might only measure up to 300 lbs. If a 350 lb individual is weighed using the scale, the observer would only know that the individual's weight is at least 300 lbs.
Censoring should not be confused with the related idea truncation. With censoring, observations result either in knowing the exact value that applies, or in knowing that the value lies within an interval. With truncation, observations never result in values outside a given range – values in the population outside the range are never seen or never recorded if they are seen. Note that in statistics, truncation is not the same as rounding.
The problem of censored data, in which the observed value of some variable is partially known, is related to the problem of missing data, where the observed value of some variable is unknown.
Interval censoring can occur when observing a value requires followups or inspections. Left and right censoring are special cases of interval censoring, with the beginning of the interval at zero or the end at infinity, respectively.
Leftcensored data, is observed, for example, in environmental analytical data where trace concentrations of chemicals may indeed be present in an environmental sample (e.g., groundwater, soil) but are "nondetectable," i.e., below the detection limit of the analytical instrument or laboratory method. Estimation methods for using leftcensored data vary, and not all methods of estimation may be applicable to, or the most reliable, for all data sets.^{[1]}
One of the earliest attempts to analyse a statistical problem involving censored data was Daniel Bernoulli's 1766 analysis of smallpox morbidity and mortality data to demonstrate the efficacy of vaccination.^{[2]}
Reliability testing often consists of conducting a test on an item (under specified conditions) to determine the time it takes for a failure to occur.
An analysis of the data from replicate tests includes both the timestofailure for the items that failed and the timeoftesttermination for those that did not fail.
Special techniques may be used to handle censored data. Tests with specific failure times are coded as actual failures; censored data are coded for the type of censoring and the known interval or limit. Special software programs (often reliability oriented) can conduct a maximum likelihood estimation for summary statistics, confidence intervals, etc.
