Cognitive BiasesPublic knowledge library

Practice Lab

Defaults, settings & choice architecture

Learn to connect a practical check with the evidence-reviewed lens behind it. A real situation can involve several patterns, so each exercise asks for the best first lens among the listed options, not a diagnosis.

How to use this set

Read the check. Choose a lens. Then inspect the evidence.

Use these evidence-reviewed lenses when a form, product, policy or system decides what happens if a person does nothing, or when an existing setting is difficult to reconsider.

Read the full decision guide before or after the set.

Exercise 1 of 4

In defaults, settings & choice architecture, which lens does this check belong to: “What happens automatically if the person does nothing, and how much does that pre-selection change uptake?”

  1. Default Effect
  2. Status Quo Bias
  3. Framing Effect
Show the best first lens

Default Effect

What happens automatically if the person does nothing, and how much does that pre-selection change uptake?

Evidence note Pre-selecting an option often increases the chance that people choose it, but the size of the default effect varies greatly across domains and designs. A 2019 meta-analysis of 58 studies found a substantial average effect together with strong heterogeneity, including some studies with little or even reversed effects. Staying with a default should therefore not be treated as a direct measure of strong preference or as proof that the default improved welfare.

Read the evidence review · Open the decision guide

Exercise 2 of 4

In defaults, settings & choice architecture, which lens does this check belong to: “Is the current option getting extra weight simply because it is already in place?”

  1. Framing Effect
  2. Anchoring Effect
  3. Status Quo Bias
Show the best first lens

Status Quo Bias

Is the current option getting extra weight simply because it is already in place?

Evidence note Status quo bias describes extra preference for an option because it is the current or existing state. Classic experiments and field observations found disproportionate persistence with status quo options, but keeping the current option is not automatically a bias. Switching can have real financial, practical, learning or uncertainty costs that make staying reasonable.

Read the evidence review · Open the decision guide

Exercise 3 of 4

In defaults, settings & choice architecture, which lens does this check belong to: “Would the choice change if the same outcomes were described with an equivalent gain, loss or attribute frame?”

  1. Default Effect
  2. Framing Effect
  3. Anchoring Effect
Show the best first lens

Framing Effect

Would the choice change if the same outcomes were described with an equivalent gain, loss or attribute frame?

Evidence note Framing research covers several distinct paradigms. Risky-choice framing, where equivalent outcomes are presented as gains or losses, is a well-established effect with substantial evidence across many studies. A classic meta-analysis found a reliable small-to-moderate average effect with strong variation across designs, and later reviews and metastudies support broad generalizability. Attribute framing also has supporting evidence, while goal or message framing is less consistently established. These should not be treated as one identical effect caused by any change in wording.

Read the evidence review · Open the decision guide

Exercise 4 of 4

In defaults, settings & choice architecture, which lens does this check belong to: “Did a suggested number, threshold or starting value become the reference point for the decision?”

  1. Anchoring Effect
  2. Default Effect
  3. Status Quo Bias
Show the best first lens

Anchoring Effect

Did a suggested number, threshold or starting value become the reference point for the decision?

Evidence note Anchoring is a well-supported effect in which an initial numerical value can pull a later estimate toward it. A 2026 meta-analysis covering 2,601 effect sizes found a large overall effect, but also substantial variation across studies. The effect should not be treated as a rule that every number changes every judgment: incidental anchors, anchors from a different dimension, clearly random values, incentives, and some debiasing conditions were associated with smaller or null effects.

Read the evidence review · Open the decision guide