After several years of experience working with, training and coaching teams on Scrum, I have no doubt that agile (empirical) estimation techniques are better than classic, predictive estimation techniques. One question that kept nagging me over time is whether or not using story points, T-shirt sizes, or ideal hours truly explains the increased success of Scrum based techniques?
It took me a while, but I finally figured it out. And the answer is a definite ‘NO’!
All Estimation Techniques are Merely Guesses
At the end of the day (or at least, at the end of the planning session), any estimation is just a guess: sometimes its more, and sometimes its less. This means that the distribution of the actual time versus the estimation should be pretty much normal: In large numbers, about half of the actual results will be less than the estimation, and half will be more. On average, according to the Law of Large Numbers, it evens up.
The better the estimation technique is, the steeper the curve will be, i.e. the closer actual results will be to the estimation.
My experience is that agile techniques are in fact better at predicting the actual results, but the fact remains that if you make enough of these predictions, then regardless of the technique, the variance should cancel itself out and the sum should settle on the average!
This means that the quality of the technique used to estimate each task has little or no impact of the success of estimating the time to complete the entire project!
Then Why Do Estimations Fail?
If estimations behave “normally” whole project estimations should not fail. I don’t buy into all that “programmers are notoriously optimistic” sentiment; I’m a developer myself and there is not a single soul alive that will call me optimistic, and yet, non-agile projects I worked on overran their deadlines, like everybody else’s. Something must be out there, that either changes the normal distribution, or invalidates the law of large numbers or both. And it has to be something that is present in classic waterfall-ish projects but not in agile ones.
It Is All About the Independence!
The main difference, in my opinion between agile and non-agile projects is how they are planned and constructed. An agile project plan is basically a stack of user stories, with no* dependencies between them, whereas a waterfall project plan is a Gantt chart describing tasks and their interdependencies.
Why Is This Important?
Let’s assume a task that has two dependencies. Each were estimated, and each have a normal distribution of the actual amount of time to complete vs. the estimated time. The following figure describes such a “plan”:
Each task has a 50% chance to be completed before time and 50% to be completed after (There is of course no statistical chance of being completed “on time”). Now, let’s say we’re interested in calculating the probability of Task C to be completed before time, or not to finish late. For the sake of ease, we’ll assume all three tasks to have the same estimated duration. For Task C to begin early, it needs both of its dependencies to complete early. Since the chance of each to complete early is 50%, the chance for both is (0.5 * 0.5) = 0.25, or 25%. The chance for this not to happen, i.e. for at least one of them to be completed late is 1-0.25 or 75%. this means that there’s a 75% for Task C to complete late, and of course it has its own probability of being late, which means that the chance of Task C completing late (and making the whole project run late) is more than 75%!
It is easy to see that the more complex a project is, the more dependencies each task has the greater the chance of being late is; The distribution of the actual duration of tasks with dependencies is a negatively skewed distribution.
Scrum User Stories Have No Dependencies
With this in mind, Scrum user stories have no* interdependencies. If two stories do have a dependency, they are should be joined to be treated as one (and if they are too big, they can and should be split again, though in a way that doesn’t create dependencies). With no dependency (or, if you wish, a single dependency that is the completion of the prior story), we get a plan that looks like this:
The chance of B starting early, which is the same chance of A ending early, is 50%. The chance of B completing early is still 50%! Even better the variance is reduced, which means that there is an increased probability of the total project duration being close to the the estimate (i.e. a steep probability distribution).
What Does This Mean?
This means, that while a project with multi-dependent tasks has an increased skew towards lateness with each added task, a project with no dependency (beyond sequence) has an increased tendency towards the mean, or the estimate.
This means that by the very nature of what we are estimating, agile projects tend to succeed (i.e. be on budget and on time), than non-agile.
Moreover, the actual estimation technique doesn’t matter – it is the estimation of independent units of work that makes the difference.
Therefore, my conclusion, and advice is that the very first change a team needs to make is to start working on independent features – call them user stories, features, or MMFs – it doesn’t matter; just keep them independent of each other!
Hope this helps,