Conduct a randomness audit of outcomes you’re explaining
Before attributing an outcome to skill, strategy, or character, estimate how much of it could be chance.
Why it works
Narrative construction attributes outcomes to agents: someone’s talent, foresight, or decision-making. This attribution is psychologically satisfying because it creates a learnable lesson ("do what they did"). But many outcomes, especially in complex, noisy environments, depend heavily on factors outside anyone’s control. Explicitly estimating the randomness component prevents the narrative from crowding it out entirely.
How to do it
- After explaining an outcome, estimate: "What percentage of this outcome could plausibly be attributed to luck, timing, or context outside anyone’s control?"
- List two or three specific random or contingent factors that were present.
- Revise the lesson accordingly: what is actually learnable vs what was fortunate circumstance?
Evidence
Attribution theory and outcome bias research show that people systematically over-attribute good outcomes to skill and bad outcomes to situational factors (or vice versa depending on identity involvement). Taleb’s contribution is emphasizing the scale of randomness in complex systems. (observational)
Estimating the "randomness percentage" is necessarily rough; the tool’s value is directional — introducing the question at all — rather than precise.
Common mistake
Applying the randomness audit only to other people’s successes ("they got lucky") and not to your own.
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More practices for The Narrative Fallacy: Why We Can’t Stop Making Stories
- Identify what the narrative leaves out
After hearing or constructing a causal story, deliberately list the facts it doesn’t explain.
- Generate alternative histories for past outcomes
For any past success or failure, construct two or three plausible alternative paths that could have led to a different outcome.
- Run a pre-mortem to interrupt forward narrative construction
Before committing to a plan, imagine it failed completely — then generate the most plausible story of why.
- Separate the data from the narrative you’ve built around it
List the raw facts, then list the story you’ve layered on top — and check whether the story actually follows.
- State a prediction before looking at the outcome
Commit to a prediction before the result is known to prevent the narrative from rewriting your memory of your forecast.
- Be skeptical of clean lessons extracted from messy outcomes
When a story produces a neat takeaway, ask whether the lesson is actually in the data or in the narrative shaping.
Related concepts
- Thinking, Fast and Slow, Made Usable
Two systems, the biases they create, and when to slow down
- Base-Rate Neglect: Why We Ignore the Odds
How to let prior probabilities do their real work in your decisions
- Hindsight Bias: Why Everything Seems Obvious in Retrospect
How "I knew it all along" corrupts judgment — and the practices that correct it