I believe what the parent is referring to is that the sum of two normally distributed variables can be modeled as normally distributed[1]. Ie: normal distributi

I believe what the parent is referring to is that the sum of two normally distributed variables can be modeled as normally distributed[1]. Ie: normal distributions are closed under addition. This fact leads to one's naive idea about adding variances working as expected. However, in task estimation[duration] aren't normally distributed - they're much more log-normally distributed, and you can't simply add log-normal distribution parameters in the same way[2]. Instead he log-normal distribution is (largely) closed under multiplication which is fairly useless when we want to add tasks to determine total time. Moreover, the reductive conceptualization of normally distributed estimations leads people to the erroneous communication of "X plus or minus Y". The assumption is that the bounds are symmetric(they aren't). The chance that the estimate is higher than the mode is much greater than it is lower than the mode. As a data scientist, I want to say that the model for task estimation is over-reduced. Even so, I feel like the push-back I'd get from suggesting that folks estimate in log-space would be that I'm being ridiculous complicated. 1. https://en.wikipedia.org/wiki/Sum_of_normally_distributed_random_variables https://en.wikipedia.org/wiki/Sum_of_normally_distributed_ra... 2. See the Fenton-Wilkinson approximation.