The precision control feature sets how precise you want the forecast statistics to be. Crystal Ball Crystal Ball runs the simulation until the selected forecast statistics reach the required precision as determined by calculating confidence intervals.
See the Oracle Crystal Ball Statistical Guide for more information about how Crystal Ball calculates confidence intervals.
Generally speaking, as more trials are calculated, the confidence intervals narrow and statistics become more precise. The precision control feature in Crystal Ball uses this characteristic of confidence intervals to determine when a specified precision of a statistic has been reached. It compares the specified precision to the confidence interval. When the calculated confidence interval drops to less than the specified precision, the simulation stops.
For each forecast, you can specify precision in either absolute terms in units of the forecast, or in relative terms as percentages. These settings are made on the Precision tab of the expanded Define Forecast dialog or the Forecast Preferences dialog. Each method, absolute or relative, has its own benefits and drawbacks.
Specifying precision in absolute terms offers greater control of the simulation when the shape and scale of the forecast distribution is roughly known. For example, for a Gross Profit forecast (from the Vision Research model) that ranges from $25.5 to $64.0 million dollars, you can require the precision of the mean to be within plus or minus $100,000 or some other convenient measure of accuracy. However, with the same forecast range, an absolute accuracy of $1000 might require an unreasonably large number of trials to reach. So, the drawback of using absolute precision is that it might require experimentation to determine reasonable accuracy values.
Specifying precision in relative terms offers greater control of the simulation when the shape and scale of the forecast distribution are largely unknown and you are interested in the accuracy only as it relates to the overall distribution itself. In the previous Gross Profit example, you might not know or care if the distribution ranges from $25,500 to $64,000 dollars or from $25.5 to $64.0 million dollars. You might require only that the simulation's estimate of the mean fall within plus or minus 5% of itself.
You might encounter the drawback of using relative precision when the forecast statistic is close to zero. For example, suppose a forecast’s distribution straddles the break-even point of zero. A relative precision of 5% of the mean, or roughly $0.5 million, results in a very small confidence interval (relative to the full range width of $49.1 million) that might take an unexpectedly large number of trials to satisfy.
Finally, Crystal Ball combines the individual forecast precision options with the confidence level value found in the Trials tab of the Run Preferences dialog to calculate confidence intervals. Generally, it is a good idea to leave this value at 95% or 90% so that you can have a high degree of confidence that the precision requirements have been met. However, if you have a large number of forecasts defined with precision control set, you can adjust the confidence level up or down to globally change the accuracy of all forecasts together.