Which of the following quantitative risk analysis techniques relies on experience and past data to compute the probability and impact of risks on project objectives?

Probability distribution is a mathematical representation of possible outcomes and their likelihoods, used to model uncertainty statistically rather than through expert experience.

Sensitivity analysis examines how changes in individual risk variables affect overall project outcomes using mathematical models, not experience or historical data directly.

Modeling and simulation - such as Monte Carlo analysis - uses computational algorithms and statistical models to project risk outcomes, not expert experience or past data in the same direct way.

Interviewing draws on the experience and domain knowledge of subject matter experts and stakeholders, along with historical project data, to assign probability and impact values to identified risks. This makes it an experience-driven technique rather than a purely mathematical one, distinguishing it from simulation-based or statistical approaches. It is recognized in PMBOK as a data-gathering technique used during Perform Quantitative Risk Analysis.