Why software estimates are complex (from HN)

Why software estimates are complex

Yet another place where intuitions derived from the normal distribution about the behavior of distributions screws people.

An explanation I like, from michaelochurch:

      Let's say that you have 20 tasks. Each involves rolling a 10-sided die. 
   If it's a 1 through 8, wait that number of minutes. If it's a 9, wait 15 
   minutes. If it's a 10, wait an hour.
      How long is this string of tasks going to take? Summing the median time 
   expectancy, we get a sum 110 minutes, because the median time for a task is 
   5.5 minutes. The actual expected time to completion is 222 minutes, with 5+ 
   hours not being unreasonable if one rolls a lot of 9's and 10's.
      This is an obvious example where summing the median expected time for the 
   tasks is ridiculous, but it's exactly what people do when they compute time 
   estimates, even though the reality on the field is that the time-cost 
   distribution has a lot more weight on the right. (That is, it's more common 
   for a "6-month" project to take 8 months than 4. In statistics-wonk terms, 
   the distribution is "log-normal".)