Environmental Health Criteria 214

HUMAN EXPOSURE ASSESSMENT
PbB PbF PbF PbB
(µg/litre) (µg/g) (µg/litre) (µg/g)
Median 246.5 11.1 188 4.7
Mean 243 14.8 195.4 4.8
Standard deviation 70.9 10.8 49.5 1.6
Standard error 11.8 1.8 11.4 0.4
Minimum 96 2.9 112 2.8
Maximum 385 49.6 297 9.8
Range 289 46.7 185 7
we can infer about the other variables. Both numerical and graphical
techniques may be used to characterize the sample and any relevant
subsets, and to obtain preliminary results from the study.
4.2.1 Numerical summaries
Numerical approaches include calculating descriptive statistics
that describe the distribution of a variable (e.g., blood lead
concentrations) in terms of central tendency and dispersion as well as
descriptions of associations between pairs of variables. Other
numerical descriptive measures can be used to describe points in the
distribution (e.g., percentiles). Each of these descriptive statistics
is described below and where appropriate the formulas used to
calculate them are provided in Table 9.
Standard measures of central tendency include the sample
median (i.e., midpoint observation) and sample mean (i.e.,
average). Referring to the lower half of Table 8, note that the median
blood lead concentration for the Maltese study population was 246.5
µg/litre, intermediate between the eighteenth and nineteenth
observations. Thus, 50% of the individuals in this sample had a blood
level less than 246.5 µg/litre and 50% had a greater blood lead
concentration. The sample mean blood lead concentration in the Maltese
population was 243 µg/litre compared to 195.4 µg/litre in Mexico,
indicating that blood lead levels were higher in the Maltese
population. Methods for assigning confidence levels to statements such
as this are described in Section 4.4. The sample mean is more precise
for estimating the average of the distribution, but it is sensitive to
measurement imprecision, errors and extreme values. Although the
sample median is less precise for estimating the average, it is more
robust with respect to errors in the data. Therefore, when outliers or
extreme values are present, or when possible errors and contamination
in the observed data are suspected, the median is likely to be a
better descriptor of central tendency than the mean.
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