I think the important thing you brought to the table with 2-point estimates is that an estimate is just what the probability density graph implies – it’s not one number, but a distribution.
What the team might have forgotten is that if you consistently give out conservative estimates, then 90% of the tasks should be finished way early, and therefore being mostly “on time” is very poor performance.
Much better to have a system where being “on time” is a challenging but achievable objective. No blame accrues for being “late”, and some praise is due for being merely “on time”.
Earlier, Simon Baker asked: “Could you elaborate on why you think the new estimates gave your team a sense of urgency?” To which I replied:
I suppose part of the answer lies in the fact that I didn’t tell them about the buffer ;-) Seriously. I only talked about the “luck” thing: With conservative estimates and Parkinson, we never benefit from good luck (ie. when something is easier than we had imagined it would be). But we’re always hit by the slightest hint of bad luck – and in an iteration of 20 tasks, one of them is dead certain to go badly. The team had complained – at several successive iteration retrospectives – that they got squeezed at the end of the iteration, no matter what they tried. So I talked about luck. No mention of buffers. No graphs or fancy science (actually, this was the hardest part for me, a Maths PhD ;-).
Another big part of the answer is that, once we got into the iteration, they saw they were finishing tasks. The team room started buzzing a couple of days into the first iteration we tried this, because tasks were moving rapidly into the ‘Completed’ column on the board. And as more cards moved, the team realised that they were in a space they’d never been to before, and the buzz increased. I guess the Big Visible Chart showing completed tasks acted as a positive feedback loop.
As I say, the whole thread makes very interesting reading…