Lean Thinking

Published in Agile

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 -

Tuesday, 13 May 2014 00:00

Estimation Part 5 – Story Counting

Published in Agile

 

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.

Tuesday, 29 April 2014 00:00

Estimation Part 4 - T-Shirt Sizing

Published in Agile

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.

Tuesday, 08 April 2014 00:00

Estimation Part 3 - Story Points

Published in Agile

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.

Tuesday, 25 March 2014 00:00

Estimation Part 2 - Accuracy vs Precision

Published in Agile

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 -

In the fields of science, engineering, industry, and statistics, the accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity's actual (true) value. The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.

Calendar

« October 2018 »
Mon Tue Wed Thu Fri Sat Sun
1 2 3 4 5 6 7
8 9 10 11 12 13 14
15 16 17 18 19 20 21
22 23 24 25 26 27 28
29 30 31