Total Error and Data Usability

As we can see, total error consists for the most part of qualitative errors the quantifiable components constitute only its minor proportion. Qualitative errors have a much greater potential to destroy the whole sampling effort than quantifiable errors, and they are usually the leading cause of resampling and reanalysis. 

Clearly, every project team should be concerned with the effect of total error on data relevancy and validity and should make every effort to minimize it. Nevertheless, the cumulative effect of various sampling and non-sampling errors may erode the data validity or relevancy to a point that the data set becomes unusable for project 

Do the data have a well-defined need, use, and purpose  

Total error can undoubtedly affect the outcome of all four questions. Only after we have established that the collected data are appropriate for the intended use, are representative of the sampled matrix, conform to appropriate standards, and are technically and legally defensible, the data can be described as data of known quality. These are the data can be used for project decisions. 

We find the answers to the four questions in the course of the data quality assessment, which is the scientific and statistical evaluation of data to determine if data obtained from environmental data operations are of the right type, quality, and quantity to support their intended use (EPA, 1997a). Part of DQA is data evaluation that enables us to find out whether the collected data are valid. Another part of the DQA, the reconciliation of the collected data with the DQOs, allows us to establish data relevancy. Thus, the application of the entire DQA process to collected data enables us to determine the effect of total error on data usability. 

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