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Data quality is an important consideration in many engineering applicationsand projects. Data collection procedures do not always involve carefulutilization of the most precise instruments and strictest protocols. As aconsequence, data are invariably affected by imprecision and sometimes sharplyvarying levels of quality of the data. Different mathematical representationsof imprecision have been suggested, including a classical approach to censoreddata which is considered optimal when the proposed error model is correct, anda weaker approach called interval statistics based on partial identificationthat makes fewer assumptions. Maximizing the quality of statistical results isoften crucial to the success of many engineering projects, and a naturalquestion that arises is whether data of differing qualities should be pooledtogether or we should include only precise measurements and disregard imprecisedata. Some worry that combining precise and imprecise measurements candepreciate the overall quality of the pooled data. Some fear that excludingdata of lesser precision can increase their overall uncertainty about resultsbecause lower sample size implies more sampling uncertainty. This paperexplores these concerns and describes simulation results that show when it isadvisable to combine fairly precise data with rather imprecise data bycomparing analyses using different mathematical representations of imprecision.Pooling data sets is preferred when the low-quality data set does not exceed acertain level of uncertainty. However, so long as the data are random, it maybe legitimate to reject the low-quality data if its reduction of samplinguncertainty does not counterbalance the effect of its imprecision on theoverall uncertainty.

Should data ever be thrown away? Pooling interval-censored data sets with different precision