Part C | 26.A
588 Part C Coastal Design
a predicted value based on past data that provides
the probability distribution and variance of the specific parameter of interest.
Error: Error itself has multiple meanings. In the
modeling/predictive sense, the concept of error requires that there be a correct answer against which
to compare a predicted answer (modeling context)
or sample result (statistical context). Error is then
defined as the difference between the actual and
predicted answer or measured sample. Therefore,
in the sense of predictive risk assessment (looking
forward in time), error is not a useful concept, as
there is not yet any actual or true value against
which to measure or compare. Error should not
be confused with mistake, which is the result of
an incorrect assumption, calculation or model formulation. The hallmark of a mistake is that it is
avoidable.
Error (Types I and II): Type I error and Type II error are technical terms used in statistics to describe
particular types of erroneous results in a testing process. The terms relate to the acceptance or rejection
of the hypothesis being tested (the null hypothesis). If the null hypothesis was rejected (found to
be false) when it actually is true, then a Type I error
occurred. Conversely, if the null hypothesis is not
rejected (found to be true) when it actually is not
true, a Type II error has occurred. These definitions
correlate to the concepts of false positives (Type 1
error) and false negatives (Type II error) in testing.
For a good technical tutorial on Type I and Type II
errors [26.87].
Exposure: The process of the receptor coming into
contact with the hazard.
Exposure pathway: The route by which the receptor
is exposed. For animals, this is either by ingestion,
inhalation, or dermal absorption.
Exposure assessment: Quantitative analysis of
how much (concentration, duration) of the hazard
reaches the receptor.
Hazard/stressor: An event (storm, accident), agent
(chemical, radiation), situation or action with a potential for an undesirable consequence, such as
harm to property, the environment, and human
health or life. This term is often used synonymously
with threat.
Probability: In its most basic sense, probability is
the chance or likelihood that something will happen.
Qualitatively, the more likely an event is to happen, the more probable the event is. Quantitatively,
probability is a value between 0 and 1, with 1 representing absolute certainty of the event occurring.
The probability of an event is typically measured as
the ratio of the number of times an event occurred
over the total number of times the event could have
occurred. For example, if we consider the event to
be the occurrence of precipitation on any given day,
we would collect information on whether it rained
on a given day for a period of time (say 1 year). The
probability of rain would then be
probability of precipitation
D
number of days with precipitation
total number of days
:
Probability distribution: The basic definition of
probability works for discrete events, but when the
event or parameter being evaluated is continuous in
nature (the maximum temperature on any given day,
the stage of a river on any given day), then a single
probability is not sufficient. Instead, possible values
are grouped into discrete ranges, and the number of
occurrences in that range are counted then divided
by the total number of measurements.
Receptor: A receptor is the specific thing or entity
being affected by the hazard/stressor. In a human
health risk assessment – the receptor is a person.
Risk: The potential for realization of unwanted, adverse consequences to human life, health, property,
or the environment. The estimation of risk is usually
quantified using the expected value of the conditional probability of the event occurring times the
consequence of the event given that it has occurred.
This definition is currently used by the Society of
Risk Analysis. Mathematically, this is expressed
as
R D P.A/ P.BjA/ ;
(26.23)
where R D risk (a probability from 0 to 1), P.A/ D
probability of event (A) occurring, and P.BjA/ D
probability of a consequence (B) occurring given
that event A occurred.
This technical definition is what will be used
through this handbook. However, there are other
definitions in other fields of which the knowledgeable practitioner should be aware. Table 26.2
presents a summary of other definitions used in various fields and a list of citations to which one may
refer for more information. In each case, the same
mathematical representation can be used, but there
are assumptions made that result in the definition
being slightly different. This is also presented in Table 26.2.
Besides having multiple potential definitions, risk
can also be differentiated by types. Different types
of risk, even if they are of the same quantitative
value, are often managed differently or even ig-
588 Part C Coastal Design
a predicted value based on past data that provides
the probability distribution and variance of the specific parameter of interest.
Error: Error itself has multiple meanings. In the
modeling/predictive sense, the concept of error requires that there be a correct answer against which
to compare a predicted answer (modeling context)
or sample result (statistical context). Error is then
defined as the difference between the actual and
predicted answer or measured sample. Therefore,
in the sense of predictive risk assessment (looking
forward in time), error is not a useful concept, as
there is not yet any actual or true value against
which to measure or compare. Error should not
be confused with mistake, which is the result of
an incorrect assumption, calculation or model formulation. The hallmark of a mistake is that it is
avoidable.
Error (Types I and II): Type I error and Type II error are technical terms used in statistics to describe
particular types of erroneous results in a testing process. The terms relate to the acceptance or rejection
of the hypothesis being tested (the null hypothesis). If the null hypothesis was rejected (found to
be false) when it actually is true, then a Type I error
occurred. Conversely, if the null hypothesis is not
rejected (found to be true) when it actually is not
true, a Type II error has occurred. These definitions
correlate to the concepts of false positives (Type 1
error) and false negatives (Type II error) in testing.
For a good technical tutorial on Type I and Type II
errors [26.87].
Exposure: The process of the receptor coming into
contact with the hazard.
Exposure pathway: The route by which the receptor
is exposed. For animals, this is either by ingestion,
inhalation, or dermal absorption.
Exposure assessment: Quantitative analysis of
how much (concentration, duration) of the hazard
reaches the receptor.
Hazard/stressor: An event (storm, accident), agent
(chemical, radiation), situation or action with a potential for an undesirable consequence, such as
harm to property, the environment, and human
health or life. This term is often used synonymously
with threat.
Probability: In its most basic sense, probability is
the chance or likelihood that something will happen.
Qualitatively, the more likely an event is to happen, the more probable the event is. Quantitatively,
probability is a value between 0 and 1, with 1 representing absolute certainty of the event occurring.
The probability of an event is typically measured as
the ratio of the number of times an event occurred
over the total number of times the event could have
occurred. For example, if we consider the event to
be the occurrence of precipitation on any given day,
we would collect information on whether it rained
on a given day for a period of time (say 1 year). The
probability of rain would then be
probability of precipitation
D
number of days with precipitation
total number of days
:
Probability distribution: The basic definition of
probability works for discrete events, but when the
event or parameter being evaluated is continuous in
nature (the maximum temperature on any given day,
the stage of a river on any given day), then a single
probability is not sufficient. Instead, possible values
are grouped into discrete ranges, and the number of
occurrences in that range are counted then divided
by the total number of measurements.
Receptor: A receptor is the specific thing or entity
being affected by the hazard/stressor. In a human
health risk assessment – the receptor is a person.
Risk: The potential for realization of unwanted, adverse consequences to human life, health, property,
or the environment. The estimation of risk is usually
quantified using the expected value of the conditional probability of the event occurring times the
consequence of the event given that it has occurred.
This definition is currently used by the Society of
Risk Analysis. Mathematically, this is expressed
as
R D P.A/ P.BjA/ ;
(26.23)
where R D risk (a probability from 0 to 1), P.A/ D
probability of event (A) occurring, and P.BjA/ D
probability of a consequence (B) occurring given
that event A occurred.
This technical definition is what will be used
through this handbook. However, there are other
definitions in other fields of which the knowledgeable practitioner should be aware. Table 26.2
presents a summary of other definitions used in various fields and a list of citations to which one may
refer for more information. In each case, the same
mathematical representation can be used, but there
are assumptions made that result in the definition
being slightly different. This is also presented in Table 26.2.
Besides having multiple potential definitions, risk
can also be differentiated by types. Different types
of risk, even if they are of the same quantitative
value, are often managed differently or even ig-