Which statement about random variables in a simulation is true?

Which statement about random variables in a simulation is true?

• A. They describe deterministic inputs.
• B. They are always integers.
• C. They must be fixed before modeling.
• D. They represent uncertain parameters that cannot be known with certainty.

Answer: (D)

In simulation, random variables are used to capture uncertainty in inputs. A random variable is a quantity whose value isn’t known with certainty and is described by a probability distribution. By assigning distributions to inputs and drawing samples from them during each simulation run, you generate different outcomes that reflect real-world variability. This is why the statement that random variables represent uncertain parameters that cannot be known with certainty is the best description—they encode the lack of precise knowledge about those inputs, rather than fixed numbers. They aren’t restricted to integers—their distributions can be continuous or discrete, depending on the nature of the quantity. They also don’t have to be fixed before modeling; you typically define a distribution to represent your uncertainty and then sample from it during runs to explore possible scenarios. Deterministic inputs, in contrast, would produce the same result every time and don’t reflect uncertainty.

In simulation, random variables are used to capture uncertainty in inputs. A random variable is a quantity whose value isn’t known with certainty and is described by a probability distribution. By assigning distributions to inputs and drawing samples from them during each simulation run, you generate different outcomes that reflect real-world variability. This is why the statement that random variables represent uncertain parameters that cannot be known with certainty is the best description—they encode the lack of precise knowledge about those inputs, rather than fixed numbers.

They aren’t restricted to integers—their distributions can be continuous or discrete, depending on the nature of the quantity. They also don’t have to be fixed before modeling; you typically define a distribution to represent your uncertainty and then sample from it during runs to explore possible scenarios. Deterministic inputs, in contrast, would produce the same result every time and don’t reflect uncertainty.