Recency bias
Recency bias is the tendency to give the most recent events disproportionate weight in judgements and predictions — assuming what just happened will keep happening, while older but relevant information quietly fades from view.
Why it happens
Recent events are easier to recall (an availability effect) and feel more relevant, so the mind extrapolates short-term trends and underweights the longer history that should inform the estimate.
Examples
- Expecting a rising market to keep rising because it rose lately.
- Judging an employee by their last week rather than the whole year.
- Predicting form or weather from the single latest result.
How to counter it
- Zoom out to the full historical record, not just the latest stretch.
- Weight base rates over recent streaks.
- Ask whether a “trend” is real signal or merely recent noise.
The deeper point
Recency bias is extrapolation disguised as observation. The recent trend feels like data, but you’re assuming the last stretch is the whole story — which is why every bubble looks obvious only after it bursts.
Frequently asked
- What is recency bias?
- Overweighting what happened most recently — treating the latest result, mood, or trend as more representative than the longer track record actually warrants.
- How does recency bias affect investing?
- It makes people chase whatever rose lately and flee whatever fell, extrapolating short streaks into the future and ignoring long-run base rates.
- How do you counter recency bias?
- Deliberately consult the full history, anchor on base rates rather than recent streaks, and ask whether a pattern is genuine signal or just noise.
Related
Editorial synthesis © ReadGlobe 2026, drawing on Kahneman’s Thinking, Fast and Slow, the Tversky–Kahneman research program, and the primary cognitive-science literature. · Last reviewed 2026-05-29.