A year on, Maya's best decision is still paying off. Then a colleague asks her why she made it.
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Right For The Wrong Reason — Volume I, Understanding Humans Before Products · Capstone · Historical · Practitioner. Maya revisits a decision that worked, finds the reason she gave at the time is not the one she remembers, and realises nothing in her process would ever have told her
01 · Opening
A year on, Maya's best decision is still paying off. Then a colleague asks her why she made it.
02 · The setup
A year on from the survey that started all this. Maya is writing the annual review and it is going well.
Maya thinks: Dropped export v2, put the team on onboarding. Churn down four points. Best call I made.
03 · The setup
The year after the decision — Churn: down 4.1 pts; Onboarding completion: up 22 pts; Export v2, still not built: correct call; Reasoning recorded at the time: none
04 · The evidence
Priya asks a straightforward question and Maya cannot answer it.
Priya: Why did you kill bulk export? I want to use the same reasoning on something.
05 · The evidence
Maya thinks: Because the cancelled accounts all said onboarding. That is what I remember. Is that what I thought at the time?
06 · The evidence
The thread from that week is still there. It is dated four days before she emailed a single cancelled account.
Maya thinks: I dropped it because engineering said six more weeks. The calls had not even happened yet.
07 · The evidence
What the thread actually says — Reason given at the time: cost, six more weeks; Cancelled-account calls: four days later; Reason remembered a year later: what the calls said; Outcome: good either way
08 · The evidence
Maya: I got the right answer by worrying about the wrong thing.
Dev: It still worked.
09 · You make the call
The story does not tell you first.
A. The rule that produced it will fail on a case where cost and value diverge
B. It does not — the outcome is what counts
C. It shows she should be more humble about her decisions
10 · What happened
It worked, and the reasoning that produced it would fail the moment a bad idea happened to be cheap.
Maya thinks: Cost as a proxy for value. That rule kills good expensive work and ships bad cheap work.
11 · What happened
Priya: So the story you have been telling for a year is one you invented afterwards.
Maya: I did not invent it on purpose. That is the part that bothers me.
12 · What happened
Write the reason down before the outcome exists — One line at the time of the decision, and the prediction that goes with it. Memory rewrites reasoning to fit results, without asking permission and without feeling like invention.
Grade the two separately — A good outcome from bad reasoning is a warning, not a win. A bad outcome from sound reasoning is often the correct call. Scoring only outcomes teaches you nothing, because the loudest signal is luck.
Look hardest at the ones that worked — Failures get examined automatically. Successes never do, so a flawed rule survives inside them until it meets a case where it costs something.
13 · What happened
The template gains two lines. What we think will happen, and why we think so, written before anyone knows.
Priya: So next year I can actually reuse your reasoning.
Maya: Or find out it was never worth reusing.
14 · What happened
Maya thinks: A whole year of being right about something I never actually noticed.
15 · Complete
Outcomes are a poor signal for judgment, because luck is louder than reasoning and only failures get examined. Volume II starts where this leaves off, with products rather than the people using them.