>> A lot of this stems from the accepted norm that an estimate is a single value. If we want to estimate accurately, it is necessary to break work down into sm

>> A lot of this stems from the accepted norm that an estimate is a single value. If we want to estimate accurately, it is necessary to break work down into smaller pieces. However, we can only estimate the items that we can identify at the time of estimation (I.e., we know that we need to do A,B and C but discover that D,E and F are needed as well). The other factor is combinatorial explosion: we'll do A,B or C and this impacts what we do next. This graph can become enormous when attempting to construct up-front. I agree that having a probability distribution for each estimate is better than a single value. If you have two values, this is a uniform distribution. It is also possible to do a normal distribution, triangular, truncated normal, etc. However, it isn't clear to me that increased sophistication leads to better outcomes.