Coaching practices for How to Estimate Without Data
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For How to Estimate Without Data, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I build my estimate from my own optimistic story first and only glance at how long these things usually take at the very end as a sanity check
- I land on a number and just go with it, never pausing to ask what’s the most it could possibly be or the least
- Someone asks me how many of something there are and my mind just goes blank
- I’m staring at a question that feels totally out of reach, and it never occurs to me that I already know one solid number nearby that I could just scale up or down from
- I’m late on something, I shrug, update the calendar, and immediately forget I ever thought it’d take half as long
Practices that may help
- Anchor on the base rate before adding inside-view details
Start your forecast from the class median, then adjust — do not start from your narrative and adjust to the base rate.
Reference Class Forecasting - Sanity-check against known extremes
Test your estimate against the clearly too-high and too-low bounds to calibrate your range.
Fermi Estimation - Decompose the unknown into knowable sub-problems
Break the question you cannot answer directly into smaller questions you can.
Fermi Estimation - Anchor on what you know and scale from there
Start from a number you are confident about, then reason to the unknown.
Fermi Estimation - Log actual vs. estimated time for every task
Build a personal database of your own estimation errors so you can calibrate future predictions.
The Planning Fallacy — Why Your Estimates Are Always Wrong - Use reference classes to ground personal estimates in base rates
Before estimating how your situation will unfold, find similar past situations and check what happened.
Bayesian Thinking: How to Update Beliefs Rationally - Fermi Estimation
Fermi estimation is the practice of making rough but principled quantitative estimates by decomposing an unknown into knowable sub-problems, estimating each, and combining them. Named for physicist Enrico Fermi, who was renowned for accurate estimates from minimal data, it is used in science, engineering, and everyday decisions to calibrate intuitions and check whether a number is in the right ballpark — not to achieve false precision. - Decompose complex questions into sub-questions
Break a hard forecasting question into smaller, estimable pieces and aggregate them.
Superforecasting - Track your estimates and calibrate
Compare your Fermi estimates to actual figures when you can, and use the gap to improve future estimates.
Fermi Estimation - Reference class forecasting
Before estimating, look up how long similar past projects actually took — not how they felt.
The Planning Fallacy — Why Your Estimates Are Always Wrong
Related concerns
- Fermi Estimation When Starting Out
Compare your Fermi estimates to actual figures when you can, and use the gap to improve future estimates.
Track your estimates and calibrate
- How To Estimate Anything
Test your estimate against the clearly too-high and too-low bounds to calibrate your range.
Sanity-check against known extremes
- Bottom Up Estimation
Estimate each sub-task independently, then add them up — the sum is closer to truth than a top-down estimate.
Decompose tasks and sum the pieces
- Decomposition Estimation
Fermi estimation is the practice of making rough but principled quantitative estimates by decomposing an unknown into knowable sub-problems, estimating each, and combining them. Named for physicist Enrico Fermi, who was renowned for accurate estimates from minimal data, it is used in science, engineering, and everyday decisions to calibrate intuitions and check whether a number is in the right ballpark — not to achieve false precision.
- Fermi Estimation At Work
Fermi estimation is the practice of making rough but principled quantitative estimates by decomposing an unknown into knowable sub-problems, estimating each, and combining them. Named for physicist Enrico Fermi, who was renowned for accurate estimates from minimal data, it is used in science, engineering, and everyday decisions to calibrate intuitions and check whether a number is in the right ballpark — not to achieve false precision.
- Fermi Estimation When Overwhelmed
Fermi estimation is the practice of making rough but principled quantitative estimates by decomposing an unknown into knowable sub-problems, estimating each, and combining them. Named for physicist Enrico Fermi, who was renowned for accurate estimates from minimal data, it is used in science, engineering, and everyday decisions to calibrate intuitions and check whether a number is in the right ballpark — not to achieve false precision.
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