well supported, highly context-dependentEvidence What happens automatically if the person does nothing, and how much does that pre-selection change uptake?
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.
well established, broader than interface defaultsEvidence Is the current option getting extra weight simply because it is already in place?
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.
risky-choice framing is robust; broader framing types have different evidenceEvidence Would the choice change if the same outcomes were described with an equivalent gain, loss or attribute frame?
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.
well-supported for numerical judgments; strength depends on anchor type and contextEvidence Did a suggested number, threshold or starting value become the reference point for the decision?
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.
