Quality Assurance (QA)
Refers to a broad plan for maintaining quality in all aspects of a program, including all quality
control measures, sample collection, sample analysis, data management, documentation,
evaluation, etc. It is helpful to data users in determining the integrity (soundness) of data.
control measures, sample collection, sample analysis, data management, documentation,
evaluation, etc. It is helpful to data users in determining the integrity (soundness) of data.
Quality Control (QC)
The steps, including measurements, calibrations, and standardization practices, taken to assure
the quality of specific sampling and analytical procedures. QC is used to reduce error in the
data collection and analysis. For example, the collection of two samples (QC samples) taken at
the same time and location should yield the same (or very similar) results; data quality can be
determined by evaluating the results of the QC samples and determining precision and
accuracy. The decision to accept data, reject it, or accept only a portion of it should be made
after analysis of the QC data.
the quality of specific sampling and analytical procedures. QC is used to reduce error in the
data collection and analysis. For example, the collection of two samples (QC samples) taken at
the same time and location should yield the same (or very similar) results; data quality can be
determined by evaluating the results of the QC samples and determining precision and
accuracy. The decision to accept data, reject it, or accept only a portion of it should be made
after analysis of the QC data.
Quality Assurance Project Plan (QAPP)
The formal written document describing the detailed quality assurance procedures and QC
activities that will be used to assure data quality.
activities that will be used to assure data quality.
Precision
Degree of agreement among repeated measurements. Reproducible results are precise. Can
be calculated using the standard deviation (a statistical way to measure variation around the
data set’s average value).
be calculated using the standard deviation (a statistical way to measure variation around the
data set’s average value).
Accuracy
Measures how close your results are to a true value. The smaller the difference between the
measurement and its “true” value, the more accurate the measurement. Found by analyzing a
standard or reference sample (one with a known value).
measurement and its “true” value, the more accurate the measurement. Found by analyzing a
standard or reference sample (one with a known value).
Completeness
The number of samples and documentation needed to meet the sampling objectives.
Volunteers may not be able to collect as many samples as planned so try to take more samples
than you expect to need.
Volunteers may not be able to collect as many samples as planned so try to take more samples
than you expect to need.
Comparability
The extent to which data from one study can be directly compared to either past data obtained
in the study or from data obtained in another study.
in the study or from data obtained in another study.
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