Definition
Decisions are graded by results alone rather than by the quality of the process that produced them.
Example
A trader labels a high-risk, poor-process trade as "good" simply because it made money during a favourable market swing. Conversely, a disciplined trade that followed all rules is dismissed as "bad" because it happened to lose. Learning becomes distorted because evaluation is tied to P&L, not process.
Cognitive Driver
The mind substitutes the clarity of hindsight for the uncertainty present at decision time. When an outcome is known, it becomes a shortcut for assessing the decision. This collapses the distinction between skill and luck, damaging the ability to refine processes.
Market Expression
Trades that worked are over-credited. Trades that lost are discarded entirely, even if they were process-aligned. Review cycles focus on P&L instead of whether the decision was consistent with the framework. Position sizing drifts because perceived "good" trades are given more weight regardless of their underlying quality.
Trigger Conditions
- Markets with high noise-to-signal ratio
- Event-driven periods where outcomes hinge on low-probability tails
- Environments where teams emphasise P&L over decision quality
- High-stakes decisions where outcomes feel personal
- Infrequent or weak process documentation
Diagnostic Markers
- Review notes dominated by P&L commentary
- Good outcomes retroactively framed as good decisions
- Lack of interest in analysing lucky wins
- Quick dismissal of framework-consistent losing trades
- Over-sizing based on a handful of favourable outcomes
Cost Profile
- False learning loops driven by randomness
- Miscalibrated confidence in strategies or signals
- Overexposure to high-variance ideas
- Weak process discipline and rising behavioural drift
- Inability to improve because feedback is tied to luck
Differentiation From Adjacent Biases
- Not hindsight bias: hindsight distorts predictability; outcome bias distorts decision evaluation.
- Not self-attribution bias: self-attribution explains successes as skill; outcome bias judges decisions by results.
- Not confirmation bias: outcome bias substitutes results for evidence, not narratives for evidence.
Corrective Lens
Evaluate decisions strictly based on what was known at the time. Store pre-trade rationale, scenarios, and risk assessments so reviews compare outcome-independent process quality. Use probabilistic scoring and expected-value frameworks to separate skill from variance and build a cleaner learning loop.