Anchoring bias is a cognitive bias in which an initial number or piece of information influences a later judgment, even when that starting point is irrelevant or unreliable. Decision-makers tend to remain too close to this initial anchor when estimating an answer.
How Anchoring Bias Works
Anchoring is commonly explained through the anchoring-and-adjustment heuristic. A person begins with an initial value and then adjusts away from it, but the adjustment is usually insufficient.
For example, imagine being told that a jacket originally cost £300 before being asked what a reasonable sale price would be. The £300 figure becomes the anchor, making £180 appear inexpensive. Without that initial price, the same jacket might be judged as worth much less.
This process demonstrates how heuristics, or mental shortcuts, make decision-making faster but can also produce systematic errors. Anchoring is often associated with rapid, intuitive processing rather than fully deliberate reasoning.
Supporting Research
Tversky and Kahneman (1974) asked participants to spin a wheel rigged to stop at either 10 or 65. Participants then estimated the percentage of African countries in the United Nations.
Those exposed to the anchor of 10 gave a median estimate of 25%, whereas those exposed to 65 gave a median estimate of 45%. Because the wheel’s value was arbitrary, the findings suggest that irrelevant numerical anchors can influence judgments.
The controlled experiment supports a cause-and-effect relationship between the anchor and participants’ estimates. However, estimating UN membership is an artificial task, so the findings may have limited ecological validity when applied to complex real-life decisions.
A common misconception is that anchoring means people simply accept the first value given. Instead, people may adjust away from it, but usually not far enough.
IB Exam Technique
For a Cognitive Approach response, define anchoring precisely, explain the cognitive mechanism, and use Tversky and Kahneman’s procedure and findings as evidence. Explicitly link the study back to decision-making, then evaluate both its experimental control and limited ecological validity.
