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Choice-supportive bias or post-purchase rationalization is the tendency to retroactively ascribe positive attributes to an option one has selected and/or to demote the forgone options. [1]
Choice-supportive bias: The tendency to remember one's choices as better than they actually were. [153] Confirmation bias: The tendency to search for, interpret, or recall information in a way that confirms one's beliefs or hypotheses. See also under § Confirmation bias. Conservatism or Regressive bias
Choice-supportive bias leads to an increased liking of one's choices, including purchases. This seems to contradict the concept of buyer's remorse. However, this choice enhancement can collapse when presented with even minor indication that the wrong choice was made.
Choice architecture is the design of different ways in which choices can be presented to decision makers, and the impact of that presentation on decision-making. For example, each of the following: the number of choices presented [1] the manner in which attributes are described [2] the presence of a "default" [3] [4] can influence consumer choice.
In studies of the bias, options are presented in terms of the probability of either losses or gains. While differently expressed, the options described are in effect identical. Gain and loss are defined in the scenario as descriptions of outcomes, for example, lives lost or saved, patients treated or not treated, monetary gains or losses. [2]
For example, if the previous one or two flips were heads, a person affected by recency bias would continue to predict that heads would be flipped. [69] Confirmation bias. Confirmation bias is the tendency to prefer information consistent with one's beliefs and discount evidence inconsistent with them. [70] Familiarity bias
A survey using a Likert style response set. This is one example of a type of survey that can be highly vulnerable to the effects of response bias. Response bias is a general term for a wide range of tendencies for participants to respond inaccurately or falsely to questions.
Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.