Life Data Classification

Statistical models rely extensively on data to make predictions. In our case, the models are the statistical distributions and the data are the  life dataor  times-to-failure data of our product. The accuracy of any prediction is directly proportional to the quality, accuracy and completeness of the supplied data. Good data, along with the appropriate model choice, usually results in good predictions. Bad, or insufficient data, will almost always result in bad predictions.

In the analysis of life data, we want to use all available data which sometimes is incomplete or includes uncertainty as to when a failure occurred. To accomplish this, we separate life data into two categories: complete (all information is available) or censored (some of the information is missing). This chapter details these data classification methods.

Data Classification
Most types of non-life data, as well as some life data, are what we term as complete data. Complete data means that the value of each sample unit is observed or known. In many cases, life data contains uncertainty as to when exactly an event happened (i.e.when the unit failed). Data containing such uncertainty as to exactly when the event happened is termed as censored data.



Censored Data
In many cases when life data are analyzed, all of the units in the sample may not have failed (i.e. the event of interest was not observed) or the exact times-to-failure of all the units are not known. This type of data is commonly called censored data. There are three types of possible censoring schemes, right censored (also called suspended data), interval censored and left censored.