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Lecture

BIO206H5 Lecture Notes - Precautionary Principle, Statistical Significance, Goal Setting


Department
Biology
Course Code
BIO206H5
Professor
S

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Chapter 14: the precautionary principle: A guide for protecting Public Health and
the Environment:
Many environmental impacts of human activities cannot be predicated because of
the complex interactions among variables in the ecosystems.
The history and definitions of the precautionary principle:
Precautionary principle says that we should aim to anticipate and avoid damages
before they occur or detect them early. It is based on underlying values and on the
following core elements:
Potential harm predicting and avoiding harm, or identifying it early, should be a
primary concern when contemplating an action
Scientific uncertainty the kind and degree of scientific uncertainty surrounding a
proposed activity should be explicitly addressed.
Precautionary action particular activity undertaken to avoid harm, even when the
harm is not fully understood.
Elements of the Precautionary Principle:
The principle is based on recognizing that some activities may cause serious,
irreparable or widespread harm.
The principle is based on the assumption that people have a responsibility to
preserve the natural foundation of life, now and into the future.
A precautionary approach is based on determining how much harm can be avoided
rather then deciding how much harm is acceptable or how much can be assimilated.
The potential for harm:
Nothing important I see
Scientific uncertainty:
Recognizing the uncertainty and limits of sciences is central to the precautionary
principle.
Global trade and travel may introduce bacteria, viruses, insects, and other exotic
species into ecosystems.
Understanding cause and effect relationships in complex system is limited by
different kinds of uncertainties.
More complex problems have a mixture of three general kinds of uncertainty—
statically, model and fundamental—each of which should be considered before
deciding how to act.
Statistically uncertainty:
Easiest to reduce or to quantify with some precision.
Understanding the global climate impact of a proposed shift in energy policy
requires more than simply knowing the probability distribution of a single variable.
Model uncertainty:
Fundamental Uncertainty:
A study of the relationship between two variables, a correlation is said to be
established with statistical significance only if the results show the two to be linked,
independent of other factors.
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