What it means
Quantitative analysis may use ranges, scenarios, probability distributions, loss-event frequency, loss magnitude, simulations, and sensitivity analysis. The result is an estimate with assumptions and uncertainty, not a prediction of an exact future loss.
Why it matters
A quantified view can improve investment and acceptance decisions when it is based on transparent evidence and is presented alongside qualitative context.
Practical focus
- Define the decision and scenario before choosing a model
- Use ranges and distributions where precision is unsupported
- Document assumptions, data quality, and confidence
- Compare treatment cost and expected risk reduction across options
Common mistakes
- Turning ordinal risk ratings into false dollar precision
- Hiding uncertainty behind a single number
- Combining unrelated scenarios into one estimate
- Treating the model as a substitute for accountable judgment
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: