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11

Laboratory Quality Assurance

KEY WORDS bias, control limit, corrective action, precision, quality assurance, quality control, range,
X
Range chart, Shewhart chart, (X-bar) chart, warning limit.
Engineering rests on making measurements as much as it rests on making calculations. Soil, concrete,
steel, and bituminous materials are tested. River flows are measured and water quality is monitored.
Data are collected for quality control during construction and throughout the operational life of the
system. These measurements need to be accurate. The measured value should be close to the true (but
unknown) value of the density, compressive strength, velocity, concentration, or other quantity being
measured. Measurements should be consistent from one laboratory to another, and from one time period
to another.
Engineering professional societies have invested millions of dollars to develop, validate, and standard-
ize measurement methods. Government agencies have made similar investments. Universities, technical
institutes, and industries train engineers, chemists, and technicians in correct measurement techniques.
Even so, it is unrealistic to assume that all measurements produced are accurate and precise. Testing
machines wear out, technicians come and go, and sometimes they modify the test procedure in small
ways. Chemical reagents age and laboratory conditions change; some people who handle the test
specimens are careful and others are not. These are just some of the reasons why systematic checks on
data quality are needed.
It is the laboratory’s burden to show that measurement accuracy and precision fall consistently within
acceptable limits. It is the data user’s obligation to evaluate the quality of the data produced and to insist
that the proper quality control checks are done. This chapter reviews how X and Range charts are used
to check the accuracy and precision of laboratory measurements. This process is called quality control
or quality assurance.
X and Range charts are graphs that show the consistency of the measurement process. Part of their
value and appeal is that they are graphical. Their value is enhanced if they can be seen by all lab workers.
New data are plotted on the control chart and compared against recent past performance and against the
expected (or desired) performance.

Constructing X-Bar and Range Charts
The scheme to be demonstrated is based on multiple copies of prepared control specimens being inserted
into the routine work. As a minimum, duplicates (two replicates) are needed. Many labs will work with
this minimum number.
The first step in constructing a control chart is to get some typical data from the measurement process
when it is in a state of good statistical control. Good statistical control means that the process is producing
data that have negligible bias and high precision (small standard deviation). Table 11.1 shows measure-
ments on 15 pairs of specimens that were collected when the system had a level and range of variation
that were typical of good operation.
Simple plots of data are always useful. In this case, one might plot each measured value, the average
of paired values, and the absolute value of the range of the paired values, as in Figure 11.1. These plots

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