Regardless of the technique you use, the tendency in project estimation is to provide one number for each estimate. In other words, if you have 100 activities on your schedule, each activity would have one estimate associated with it. This is generally viewed as the “most likely” estimate.In many cases you can be more accurate by applying a simple PERT (Program Evaluation and Review Technique) model. PERT is an estimating technique that uses a weighted average of three numbers (see below) to come up with a final estimate.

  • The most pessimistic (P) case when everything goes wrong
  • The most optimistic (O) case where everything goes right
  • The most likely (M) case given normal problems and opportunities

The resulting PERT estimate is calculated as (O + 4M + P)/6. This is called a “weighted average” since the most likely estimate is weighted four times as much as the other two values. You’ll notice that the final PERT estimate is moved slightly toward either the optimistic or pessimistic value – depending on which one is furthest from the most likely. Generally this ends up moving the final estimate toward the worst case, since the worst case value tends to be further out from the most likely that the optimistic number.

For example, let’s say you estimate a piece of work to most likely take 10 hours. The best case (everything goes right) is six hours. The worst case (everything goes wrong) is 26 hours. The PERT estimate is (6 + 4(10) + 26)/6. The answer is 72/6, or 12 hours. Notice that the number was pulled a little toward the far extreme of the pessimistic estimate, but not by much, since the result is still weighted heavily toward the most likely value.

You can use the PERT estimates two ways. You can provide these three estimates for all activities in your schedule or you can only use the PERT formula for those activities that are of high risk. These are the ones where you’re not really sure of the estimate so there’s a wide variation between the optimistic and pessimistic values.

Speaking of variation – if you subtract your pessimistic value from the optimistic value and divide the result by six, you would have the standard deviation, which is a measure of the volatility of the estimate. In our example above, the standard deviation would be 3.34 ((26 – 6) / 6). The larger this standard deviation is, the less confidence you have in your estimate, since it would mean you have a large range between the optimistic and pessimistic estimates. If the standard deviation was small, it would mean you were pretty confident in your estimate, since the optimistic and pessimistic estimates would be close.

Remember the PERT formula and use it to make estimates when you have a high level of uncertainly.