Final answer:
In a base-case scenario, the output is determined by the most likely values for the random variables of a model, which is a pragmatic prediction. The base-case scenario differs from using the mean values or creating pessimistic (worst-case) or optimistic (best-case) scenarios. Option (d) is the correct answer for the base-case assumption.
Step-by-step explanation:
In a base-case scenario, the output of a statistical model is generally determined by the most likely values for the random variables of a model. This approach assumes neither pessimism nor optimism about the conditions being modeled. Unlike a worst-case or best-case scenario, which uses the least favorable or most favorable variables respectively, or a scenario using the mean values which represents an average case, the base-case scenario is centered around the most likely values. This does not mean that it uses the 'mean trial values' as suggested in option (b), which could imply an average of all potential outcomes. Rather, the selection of the most likely outcomes is informed by probability and experience to give a balanced and pragmatic prediction of a probable outcome.
Using the binomial probability distribution as a context, if we consider n independent trials with two possible outcomes (success with probability p, and failure with probability q), the mean or expected value of successes (µ) is obtained using the formula µ = np. Here, the mean is defined as the long-term average after repeating the trials many times. However, the base-case scenario would reflect outcomes that are deemed most probable based on the specific conditions of the experiment rather than this long-term average.
Therefore, the correct option for what the base-case scenario assumes is (d) the most likely values for the random variables of a model.