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Confidence Interval

measuring against a known quantity

When we quantify our uncertainty by expressing our estimate as a range, we are saying that we’re confident that the actual value is likely to fall somewhere in that range. You might be wondering: what do we mean by confident?

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Framing Tricks

put things in context before the guessing game part

Making range estimates to express uncertainty is an improvement over discrete value estimates because we can stop making assumptions, and more easily focus on what we’re most uncertain about. Paying attention to the 90% Confidence Interval goal keeps us honest about our uncertainty. All this is good to have under our belt, but what we still need are some techniques for validating our confidence in our estimates.

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The Art of Right-Sizing Stories

breaking down to things we can reason about

Getting story sizes right is one of the most important skills of a successful team. Too much decomposition risks losing the narrative and getting side-tracked from a focus on the value stream, while too little decomposition leaves the work at risk of going in the wrong direction or getting blocked. The essence of decomposition is breaking things down to components of work that we can reason about.

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Qualitative Decomposition

taming complexity through the qualities of things

If we’re going to leave effort estimation till the technical planning meeting, how do we measure the size of a story in the Queue Replenishment or Sprint Planning meeting? When we understand the various Methods of Measurement, then we know we have more tools in our kit than just time-based effort estimation. Nominal scales give us a way to measure specific qualities of stories. We can adopt a set of qualities as a screen for story decomposition, without having to resort to time-based effort estimations, which require more technical considerations that we have access to at this stage.

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Release Planning

queue time — foundation for predictability

To be able to estimate for release planning, you have to come to grips with queue time. With story estimation, you have to work out the range of effort likely needed to implement the task, taking into consideration it’s dependencies. But effort estimation can’t tell you how much time the work will sit in state transitions waiting for attention. To understand queue time, you have to reason about a different set of problems.

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