What are 'output confidence intervals' used for in simulation analysis?

Output confidence intervals are vital for understanding the uncertainty associated with simulation results. They provide a range within which we expect the true value of the output metric to lie, given the randomness inherent in simulation processes. By constructing these intervals, analysts can quantify the degree of uncertainty linked to the estimates produced by the simulation, allowing for more informed decision-making.

In simulation analysis, where processes often involve randomness and variability, confidence intervals help capture the potential fluctuations in outcomes rather than giving a single point estimate. This is crucial when making decisions based on simulation results, as it highlights the reliability and precision of these estimates.

By having a confidence interval, users can gauge the range of possible outcomes and the likelihood of various scenarios occurring, which is essential for risk assessment and management. This understanding is particularly useful in fields such as finance, operations, and engineering, where making decisions with incomplete information is common.