Risk Analysis is a formal discipline, and only that: it gives the analyst a rigorous form into which he casts insights, expertise, measurements, and scientific and engineering
By providing a formal structure, it leads and guides the analyst to approach problems in a reviewable and transparent way, which is just good science.
To solve large problems, software tools are necessary, but they are no substitute for intimate domain knowledge, historical knowledge of risk science, an general facility with mathematics.
Quantification, or measuring, the risk/safety of a situation is not the goal of a PRA. And to believe the numbers is folly.
The act of trying to measure the risk involved is the source of knowledge. The acts of trying to assign values, combining them, questioning their accuracy, and building the risk model are the great treasure of PRA: the key to the treasure is the treasure itself.
Uncertainty is not some noisy variation around a mean value that represents the true situation. Variation itself is nature’s only irreducible essence. Variation is the hard reality, not a set of imperfect measures for a central tendency. Means and medians are the abstractions.
Too often risk is defined as risk = likelihood * consequence and safety = 1-risk This can misinform: acceptable risk is a consideration of likelihood AND consequence, not a simple multiplication with safety as the additive inverse of risk. Acceptable risk and safety are normative notions, changing with situations and expectations, and must be assessed accordingly.
Safety cannot be measured by an absence of accidents result which is largely dependent on luck. Safety is the heir of constant, active identification of hazards and elimination. Near misses are NOT testimonials to practices.
I often say that when you can measure what you are speaking about, and express it in numbers, then you know something about it; but when you cannot measure it, when you cannot express it in numbers, your may knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science, whatever that may be. — Lord Kelvin, 1891
If you think you can measure it, then something is probably wrong, anyway. — Woody Epstein, 2010