[jrek]

The issue I've always had with Monte Carl simulation in this context is that it takes more knowledge, expertise, time, and care to accurately perform a Monte Carlo Simulation than to prepare an accurate estimate. It's like saying you can avoid building (yet another) barely functional go-kart by instead building a four-wheel drive. Monte Carlo Simulation sometimes cloaks that the assumptions behind the simulation largely determine the output and are just as (if not more) prone to error as the usual assumptions.

If the burden of accurately estimating an average duration for a blog post is too much for your planner, what are the odds they're going to accurately develop a probability distribution for the duration of a blog post?

In simple estimates there's no need to use a stochastic approach at all. For instance, the example in the post - if you know a blog post takes between 1-10 days (uniformly distributed) and that you need to get one out every ~6 days to get 60 out in a year, you already know the probability is ~60%. If you know there's a skew to the higher end and guess a distribution (as in the example), again you can directly work out that the odds of success are ~35%.

There is value when the estimate is not corrupted by other priorities (rare), when the expertise to accurately develop distributions for activities exists (also rare, particularly for work that isn't easy to sample and re-forecast), and when the plan is complex enough that it's hard to directly predict the impact of your statistical assumptions.