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The objective of a qualitative analysis is similar to that of a quantitative one. Its method of focus is
simply less precise. That is, in a qualitative analysis, a risk probability is described in accordance with the
likelihood criteria discussed in Chapter 3.
Qualitative analysis verifies the proper interpretation and application of the safety design criteria
established by the preliminary hazard study. It also verifies that the system will operate within the safety
goals and parameters established by the Operational Safety Assessment (OSA). It ensures that the search
for design weaknesses is approached in a methodical, focused way.
8.3.2 Quantitative Analysis
Quantitative analysis takes qualitative analysis one logical step further. It evaluates more precisely the
probability that an accident might occur. This is accomplished by calculating probabilities.
In a quantitative analysis, the risk probability is expressed using a number or rate. The objective is to
achieve maximum safety by minimizing, eliminating, or establishing control over significant risks.
Significant risks are identified through engineering estimations, experience, and documented history of
similar equipment.
A probability is the expectation that an event will occur a certain number of times in a specific number of
trials. Actuarial methods employed by insurance companies are a familiar example of the use of
probabilities for predicting future occurrences based on past experiences. Reliability engineering uses
similar techniques to predict the likelihood (probability) that a system will operate successfully for a
specified mission time. Reliability is the probability of success. It is calculated from the probability of
failure, in turn calculated from failure rates (failures/unit of time) of hardware (electronic or mechanical).
An estimate of the system failure probability or unreliability can be obtained from reliability data using
the formula:
P = 1-e-lt
Where P is the probability of failure, e is the natural logarithm, l is the failure rate in failures per hour,
and t is the number of hours operated.
FAA System Safety Handbook, Chapter 8: Safety Analysis/Hazard Analysis Tasks
December 30, 2000
8- 8
However, system safety analyses predict the probability of a broader definition of failure than does
reliability. This definition includes:
A failure must equate to a specific hazard
Hardware failures that are hazards
Software malfunctions
Mechanically correct but functionally unsafe system operation
due to human or procedural errors
Human error in design
Unanticipated operation due to an unplanned sequence of
events, actions or operating conditions.
Adverse environment.
It is important to note that the likelihood of damage or injury reflects a broader range of events or
possibilities than reliability. Many situations exist in which equipment can fail and no damage or injury
occurs because systems can be designed to fail safe. Conversely, many situations exist in which
personnel are injured using equipment that functioned reliably (the way it was designed) but at the wrong
time because of an unsafe design or procedure. A simple example is an electrical shock received by a
repair technician working in an area where power has not failed.
8.3.2 Likelihood of occurrence
Working with likelihood requires an understanding of the following concepts.
· A probability indicates that a failure, error, or accident is possible even though it may occur rarely
over a period of time or during a considerable number of operations. A probability cannot indicate
exactly when, during which operation, or to which person a accident will occur. It may occur during
the first, last, or any intermediate operation in a series without altering the analysis results. Consider
an example of when the likelihood of an aircraft engine failing is accurately predicted to be one in
100,000. The first time the first engine is tried it fails. One might expect the probability of the
second one failing to be less. But, because these are independent events, the probability of the second
one is still one in 100,000. The classic example demonstrating this principal is that of flipping a coin.
The probability of it landing "heads-up" is 1 chance in 2 or 0.5. This is true every time the coin is
flipped even if the last 10 trials experienced a "heads-up" result. Message: Do not change the
prediction to match limited data.
· Probabilities are statistical projections that can be based upon specific past experience. Even if
equipment is expected to perform the same operations as those used in the historical data source, the
circumstances under which it will be operated can be expected to be different. Additional variations
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