Environmental Health Criteria 214

HUMAN EXPOSURE ASSESSMENT
7 0.000070 0.9999904 0.02152 0.
8 0.0000086 0.9999989 0.00804 0.
9 0.0000009 0.9999999 0.00267 0.
10 0.00000009 0.9999999 0.00080 0.
11 0.000000008 0.9999999 0.00022 0.
12 0.000000001 0.9999999 0.00005 0.
concepts presented in Chapter 4.4.1, a single measurement can be
expected to be within approximately 3-7 µg/m 3 of the true
concentration 95% of the time, i.e., within ±2 standard deviations of
the average difference.
For a probability distribution, the coefficient of variation is
defined as the ratio of the standard deviation to the point estimate
of the mean. In this way, the coefficient of variation error describes
the degree of dispersion of a data set relative to a measure of its
central tendency. The coefficient of variation provides a quantitative
estimate of the relative degree of variability among the observations
in a data set. Using data from the hypothetical example described
above, the coefficient of variation among the pairs of duplicate
samples is 0.2. Thus, on average, a single measurement can be expected
to be within 20% of the actual concentration.
4.4.3 Hypothesis testing and two-sample problems
Exposure assessments are often performed to determine whether the
level of exposure to a pollutant is different between two or more
groups of people or locations or periods of time. Additional
attributes typically considered to be determinants of exposure include
any number of demographic factors (e.g., age, gender, ethnicity) and
behaviour patterns. This section describes the statistical procedure
used to address this type of study objective.
Statistical hypothesis testing is a procedure where a choice is
made between two hypotheses that are not weighed equally; the null and
the alternative. The null hypothesis typically reflects what can be
stated with confidence about a particular phenomenon on the basis of
existing information. In practice, concluding that the null hypothesis
is false indicates that the data provide strong evidence for a
departure from conventional wisdom or practice. Thus, hypothesis tests
are generally constructed such that the conclusion will lie with the
null unless the evidence strongly suggests otherwise.
Two types of errors can arise from hypothesis testing:
* concluding that the alternative hypothesis is true when it is in
fact false (false negative)
* concluding that the null hypothesis is true when in fact it is
false (false positive).
The first type of error is known as a type I error and the second
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