In the first post of this series, I compared Statistical PERT with @Risk’s RiskPERT function, to see how close SPERT estimates, which use the normal distribution, come to PERT estimates using the beta distribution. When I compared a skewed uncertainty where the range between the minimum point-estimate and the most likely outcome was half as much as the range between the most likely outcome and the maximum point-estimate, the SPERT estimates came within 2.5% of RiskPERT estimates.

For this post, I made a similar comparison, but this time I made the bell-shaped uncertainty even more skewed. This time, the range between the most likely outcome and the maximum point-estimate was **three times greater** than the range between the minimum point-estimate and the most likely outcome. The three-point estimate I used was: 5000-12000-33000.

When I ran the same analysis for this comparison scenario, I learned that SPERT estimates came within 4% of RiskPERT estimates. The difference was most pronounced between the 50th and 75th percentile. When the estimates were around the 80th percentile and above, the differences shrank to around 2.5% between SPERT and RiskPERT, and when the estimates were above the 90th percentile, the differences shrank to around 1.5%.

Have a look at this spreadsheet!