Estimation Part 6 - The Argument For Story Counting
In the last post, we looked at estimating by essentially not estimating. To do that we broke down stories into two categories - small and the rest. Small stories were ready to be accepted into the team's backlog, the rest were too large and need to be broken down further. By doing this, velocity becomes just a count of stories completed and all the hassles involved with story point estimation just go away.
To me, this is a real no-brainer. Why wouldn't you estimate this way? But whenever I mention this in polite company, I tend to get some uncomfortable silences, strange looks and the inevitable - "but....". These buts, tend to come in three types -
Estimation Part 5 - Story Counting
Last time we looked at T-shirt sizing and some of the benefits and problems that method has. We found that its greatest benefit was also its biggest disadvantage. The use of something completely abstract (T-shirt sizes) removes all our cognitive biases around numbers but by not using numbers we can’t really compare estimates against each other and make predictions except by converting back to numbers which of course brings our biases back.
We can use T-shirt sizes usefully if we make an adjustment to the scale we use. Rather than have Small, Medium, Large and Extra Large, let's just have Small and Extra Large. Now, this would obviously never work for clothing because people come in a range of sizes. Stories come in a range of sizes as well, so what gives? What makes this useful? The trick here is that unlike people where we can’t dictate what size someone should be (outside the modelling industry and certain trendy nightclubs), we can, and should, be pretty strict about what size a story can be before we accept it onto a sprint.
Estimation Part 4 - T Shirt Sizing
Last time we started to look at relative estimates and the most common method of relative estimation using story points. We looked at why they work well but also at some of their limitations. The biggest limitation is the fact that they are numbers and we have some built in cognitive biases when it comes to numbers. We mistake precision for accuracy and tend to agonise for ages over the story point numbers which turns story points from a fast, lightweight and accurate method of estimation into a slow, heavyweight and accurate method. It's still accurate but we waste a lot of time.
There is a way to keep the accuracy of story points but remove the cognitive biases we have around numbers. It’s a simple as not using numbers in our estimates. The usual way to do this is by using T-Shirt sizing – stories are small, medium, large or extra-large. Some teams go a bit further and add Extra Small and XXL but we’re getting into false precision there so I would recommend against that.
Estimation Part 3 - Story Points
Last time we looked at the concepts of accuracy and precision and how getting the two mixed up can lead to all sorts of problems. We also looked a little at our cognitive bias, that has us assuming that precise numbers are also automatically accurate. The upshot of that is that we humans are absolutely terrible at estimation. We mistake precision for accuracy and our accuracy is really bad to begin with.
That last statement is only half true. We are really, really bad at things like guessing how many jelly beans are in a jar, or how tall that person is, or how much does that thing weigh. What we are bad at is absolute estimates. To make up for that, we are really, really good at relative estimates.
Estimation Part 2 - Accuracy vs Precision
Last time we looked at why we estimate and why there is always pressure to make our estimates more accurate. We have come up with a vast number of methods for estimation all of which aim to improve accuracy. The problem is that most of them don't. What they improve is precision instead.
Most people think of accuracy and precision as being the same thing. But they aren't. My nerdy and pedantic engineering background tells me that accuracy is how close to the true value a measurement is, while precision is a measure of how reproducible the measurement is. A more formal definition (thanks to Wikipedia) is -
Estimation Part 1 - Why Do We Estimate?
A couple of posts back I mentioned Estimation and my desire to poke a stick at the hornet's nest that estimation can be. Estimation is always a controversial topic. It's often at the heart of serious conflicts within organisations. There are a huge number of estimation methods and techniques but nothing seems to prevent these issues from coming up. Before I poke a stick into the hornet's next (well, not so much poke as take a full bodied swing with run up and follow through), I'll spend a little while looking at why we estimate in the first place.
Any time we have two parties involved in something there is estimation happening. Right back from prehistoric times -
The Problem With Projects
They say that when all you have is a hammer, every problem looks like a nail. It’s the same in business – when all you have is a project management methodology, everything looks like a project. Most organisations have become very project focused. Everything is a project. New release of software – project. Some process change – project. That’s great. Projects are good. They are certainly better than the ad-hoc approach we had before projects. But projects do have some drawbacks.
To work out what the drawbacks are, we need to look at what a project is. A project is defined (by the PMI who should know) as something that has a defined scope, a defined start and a defined end date. So projects are finite in length. Anything without an end date isn’t a project, it's business as usual.
When You Are At The Bottom Of A Hole
When a team is behind its targets, the natural instinct is to work even harder to catch up. Sometimes though, the best thing you can do is… nothing.
Let’s look at a team. For various reasons, they have done several sprints' worth of work with no test environment available. How can this be? Let’s just imagine that they work for a hypothetical large company with a ludicrously complex process around setting up test environments. I’m sure such things never happen in real life, but just go with me on this one.
Vision
Normally, the phrase 'project vision" or "project goal" elicits a collective groan from any team in which it is used. This is because project vision statements are generally... well... crap. Whoever puts them together inevitably feels it necessary to slip into management speak and string a bunch of fairly meaningless weasel words together – "we will proactively leverage our synergies to achieve outcomes consistent with our values going forward...". Lots of words but no actual meaning. No wonder people greet them with a groan.