Scenario: Many times the models and forecasts that we create can be improved by adding additional factors and data that may have an effect. Some might say the more detailed the model, the more accurate the results.

1. Do you feel this is the case?
2. At what point should you stop improving the accuracy of a forecast and except the level of accuracy you already have?
3. Does the answer to this question vary based on the type of decision being made? How?
4. How could we categorize decisions to determine how much analysis was appropriate?