Estimation as Uncertainty Reduction
Laracon Amsterdam 2019
What is this estimation thing anyway? The whole point of estimation is to support decisions. If an estimate doesn’t help resolve uncertainty about a decision we have to make, then it doesn’t have any value. When pro forma estimates are taken as performance targets it tends to result in a negative value. But there is substantial value in the estimation process when is understood as a quantitatively expressed reduction of uncertainty based on observations.
The big takeaway we’re going for is a shift in mindset about the concept of estimation to a methodology for supporting decisions. We don’t ignore the need to eventually express things in terms of time and money, but only as an artifact of our process.
To reset our conception of estimation, you’ll learn:
- how to treat assumptions so serve to support decisions, rather than get lost in the weeds
- about how range estimation differs from discrete estimates
- how range estimates facilitate an iterative process of uncertainty reduction
- about the role of structured observations in producing the measurements the support estimates
- how information theory gives us the tools to measure and reduce uncertainty
- about the definition of information entropy and it’s significance in skillful uncertainty reduction
- how to understand the role confidence interval in qualifying uncertainty
- about the concept of calibration and how it applies to humans who attempt estimation
With the above reset of the conception of estimation, we’ll introduce a taxonomy of measurement methods. Most of the concepts we’ll cover in this section are familiar to anyone building software, but seeing it as a unified vocabulary of measurement methods allows us to drill down to essential distinctions critical to our success in estimation.
In the Methods of Measurement segment you’ll learn:
- a proper context for Rational Scale measurements.
- the nature of Interval Scale measurements.
- the characteristics of Ordinal Scale measurements.
- the value Nominal or Qualitative measurements.
- that Story Points are an Interval Scale measurement and why that matters
- how the Scrum Velocity calculation actually works
- about how to re-think tee-shirt sizing
- about the often overlooked value of Nominal scale measurements
We work through a series of practical exercises that can facilitate the application of these principles in story estimation situations.
- how to get a starting point when you have no clue
- how to avoid mid-point anchoring
- tips for validation your impulse estimate
- tricks for flushing out your actual confidence
We’ll conclude with a roadmap of additional topics in estimation practice
- why story decomposition deserves a session on its own.
- how capacity planning requires analysis beyond story estimation.
- how release planning requires analysis above and beyond story estimation and capacity planning,
- the role of Monte Carlo simulations in roadmap and project planning estimation.
- how the No Estimation movement is in many ways consistent with the concept of estimation as uncertainty reduction.
Our goal is nothing less than rebooting the conception of what estimation is. When estimation is understood as uncertainty reduction, it unlocks a whole arsenal of analytical tools that software professionals already have, but don’t typically bring to the table. Everyone says that they are bad at estimation, but many of those same people are good at analysis. We want good software developers to realize that they probably are good estimators, once the get the concept of what they’re trying to get done right.
Let's agree to define productivity in terms of throughput. We can debate the meaning of productivity in terms of additional measurements of the business value of delivered work, but as Eliyahu Goldratt pointed out in his critique of the Balanced Scorecard, there is a virtue in simplicity. Throughput doesn’t answer all our questions about business value, but it is a sufficient metric for the context of evaluating the relationship of practices with productivity.