Anchoring bias
Anchoring bias is a cognitive bias in which an initial piece of information or reference point disproportionately influences subsequent judgments or estimates.
Demand Forecasting Best Practices
Anchoring bias is the tendency for an initial number or idea to influence subsequent estimates or judgments, even when it is unrelated or weakly relevant.
- How it is used
- Protects demand-planning judgments from early numbers that can distort collaborative forecasts.
- How deep it goes
- Defines the bias, illustrates its effect, and gives procedural safeguards through independent and anonymous forecasting.
- Examples
- Uses a wheel-of-fortune estimate, a 20% sales-growth opening claim, emails, colleague remarks, and anonymous S&OP predictions.
- What you can do after
- Apply it by collecting independent forecasts before discussion and using anonymity when peer pressure persists.
Read how Demand Forecasting Best Practices explains anchoring bias →
Collaborative Software Design: How to facilitate domain modeling decisions
A cognitive bias in which an initial suggestion or piece of information disproportionately influences subsequent judgments and decisions.
- How it is used
- Improves collaborative modeling by revealing how early suggestions and status differences can steer group decisions.
- How deep it goes
- Demonstrates the bias through EventStorming and Example Mapping, then gives facilitation practices to limit its influence.
- Examples
- Uses pivotal-event selection, subgroup convergence, sticky-note ideation, visualized flows, and prepared facilitation nudges.
- What you can do after
- Practice/perform by detecting anchoring patterns and creating meeting structures that elicit individual ideas before group influence.
Related concepts
- cognitive biaskind of · A cognitive bias is a systematic tendency in human perception, judgment, or reasoning that can distort how information is evaluated and decisions are made.
- biascontrasts with · Bias is a systematic deviation or tendency in data, measurements, estimates, judgments, or outputs that skews them relative to a relevant target or impartial baseline.
- outlier removaladdressed by · The exclusion of observations identified as unusually distant from the rest of a dataset.
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