Why are our estimates always wrong
Run the check before the number, not after the miss. Most of the error is in the conditions, not the arithmetic.
Technology work carries real, honest uncertainty, and stakeholders want a precise answer about timing anyway. Made under pressure and without solid data, an estimate falls back on guessing, or on deferring to whoever is most senior in the room.
Daniel Kahneman named the underlying effect the planning fallacy: predictions about how long a future task will take reliably display optimism bias, underestimating the time the task will actually need. It is not a discipline problem and it does not respond to being asked to try harder.
There is also a newer version of the oldest bias in the room. Deferring to a confident AI-generated recommendation under pressure is deferring to the most senior voice all over again, a different and equally fallible authority delivering a single point of view with the same misplaced certainty, no title required.
What you get
- A six-item pre-estimate checklist about who is in the room and who has not spoken
- A history table with an estimated-to-actual ratio column, which is the most useful number on the page
- The question to ask when a tool produced the number
- Three moves for giving an honest estimate a stakeholder can actually use
Where should we send it?
One email with the worksheet. You can unsubscribe from anything else in one click.
On its way
Check your inbox. If it has not landed in a minute or two, look in promotions or spam, and add sam@startuppatterns.com to your contacts so the rest arrive.
Adapted from the Mindfulness chapter of The Mindful Product Company by Sam McAfee.