In Crystal Ball,Latin Hypercube sampling results in a more even distribution of forecast values than Monte Carlo forecasting because it samples the entire range of the distribution in a more consistent manner.

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Latin Hypercube sampling divides the range of values for each input variable into equal intervals and then samples randomly within each interval. This results in a more even distribution of forecast values compared to Monte Carlo forecasting, where values are sampled randomly without regard to their distribution. This makes Latin Hypercube sampling a more accurate and efficient method for forecasting in Crystal Ball.

In Crystal Ball,Latin Hypercube Sampling Results in a More Even