Which method is commonly used for numerical assessment of risk?

Numerical assessment of risk often relies heavily on statistical analysis because it provides a rigorous framework for quantifying risk through data-driven methods. Statistical analysis allows practitioners to evaluate historical data, identify trends, and calculate probabilities associated with various risks, enabling a clear numerical representation of potential outcomes and their impacts.

This method incorporates various techniques such as regression analysis, probability distributions, and forecasting models, which help in estimating the likelihood of adverse events and their financial implications. By using statistical techniques, risk managers can create metrics that inform decision-making, such as expected loss and value at risk, enhancing their ability to make informed choices.

While the other options offer valuable insights and qualitative information—like focus groups and expert panels, which gather opinions and expert advice—they do not yield the quantitative, numerical assessments that statistical analysis does. A risk matrix can visualize risks by rating their likelihood and impact, but it does not provide the comprehensive numerical analysis that is characteristic of statistical approaches. Hence, statistical analysis stands out as the method best suited for a detailed numerical assessment of risk.