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Anchoring bias

Also known as the anchoring effect · Estimation & judgement

Anchoring bias is the tendency to rely too heavily on the first piece of information encountered — the “anchor” — when making decisions. Later judgements adjust away from it but rarely far enough, so the initial number quietly skews the outcome.

Why it happens

The anchor sets a mental reference point, and adjustment from it is effortful and stops too early. Strikingly, even arbitrary or obviously irrelevant numbers pull estimates toward them — the mind grabs whatever value is present first.

Examples


  • A high “original price” making a sale price feel like a bargain.
  • The first salary figure named framing the entire negotiation that follows.
  • A random number seen just before an estimate nudging that unrelated estimate.

How to counter it


  • Form your own independent estimate before you see any anchor.
  • Deliberately consider the opposite extreme to widen your range.
  • Ask whether the reference point is actually relevant — or just the first one offered.

The deeper point

Anchors work even when you know they’re arbitrary, even when you’re warned. Awareness doesn’t immunise you — only generating your own number first does. The defence is sequence, not willpower.

Frequently asked


What is an example of anchoring bias?
A jacket marked “was $400, now $150” feels cheap because $400 anchors your sense of its value — even if $150 is still more than it’s worth.
Why does anchoring work even with irrelevant numbers?
The mind uses the first number present as a starting point and adjusts insufficiently from it. Studies show even random numbers can shift unrelated estimates.
How do you reduce anchoring bias?
Generate your own estimate first, consider an opposite extreme, and consciously question whether the anchor is meaningful or simply the first figure you happened to see.

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.