Cognitive Biases: Estimation Family
Empathetic Gap
Also known as hot-cold empathy gap
Definition:
Our behaviors, preferences, and understanding of others depend on our own emotional state. Our empathy is greater towards people who are in a similar emotional state to ours at a given moment.
Example:
During a user test, if the participant is very frustrated and shows annoyance, the facilitator may have difficulty understanding these emotions if they themselves are in a calm and relaxed state.
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Base Rate Neglect
Also known as Neglect of Sample Size
Definition:
There is a tendency to overestimate the probability of an event by neglecting important contextual factors, including the sample size.
Example:
In the analysis of individual interviews, one might think it's possible to draw generalizations about all end users when the 10 interviewees may not be a representative sample.
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Anchoring Bias
Definition:
There is a tendency to rely primarily on our initial impression (anchor) of a person or object. It can be difficult to let go of this anchor when considering new information.
Example:
When designing a website, it's important to create an attractive home page or landing page to make a good first impression on users.
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Effort Justification Bias
Definition:
The more effort something has required to obtain, the more value we tend to attribute to it, even if it is objectively negative.
Example:
Allowing users to engage in product customization tends to improve their perception of the product and the brand, even if it required effort.
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Hard-Easy Effect
Definition:
There is a tendency to show overconfidence in the completion of difficult tasks and underconfidence in completing easy tasks.
Example: Awareness of this bias can be useful in interpreting what users report about task completion, especially if it was not observed directly.
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Dunning-Kruger Effect
Also known as Overconfidence Effect
Definition:
In a given domain, people with little or no expertise tend to overestimate their level of competence and knowledge. Conversely, experts tend to underestimate their level of competence and knowledge compared to others. Experts may wrongly assume that tasks that are easy for them are easy for everyone.
Example: A designer may think that an interface they designed and are familiar with will be easy to use for everyone.
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False Consensus Effect
Also known as Social Projection Bias
Definition:
There is a tendency to overestimate the number of people who agree with us or share the same opinions, tastes, etc. This leads us to believe that our opinions or activities are much more common than they actually are.
Example:
In project management, involving users is necessary from the beginning to consider all uses and viewpoints, not just those of the team.
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Illusion of Control
Definition:
In some situations, we may think we have the power to control or influence our environment when we do not. This often happens with random events, where we believe we can favor positive outcomes and avoid negative ones.
Example:
Providing users with feedback on ongoing actions in an interface can give them a sense of control and reassurance, even if they don't actually have control.
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Impact Bias
Definition:
There is a tendency to overestimate the emotional impact of future events, both in terms of their duration and intensity.
Example:
When discussing a product or feature, it's important not to oversell it to users, as it can lead to disappointment.
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Curse of Knowledge
Also known as Curse of Expertise
Definition:
We implicitly assume that others have the same level of knowledge as we do in an area we are familiar with. This can make it difficult to empathize with beginners.
Example:
A designer who regularly designs mobile applications may have reflexes and ease of navigation that are not necessarily shared by users.
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Extrinsic Motivation Error
Definition:
Extrinsic motivations are perceived as greater in others than in oneself.
- Extrinsic Motivation: Reward or punishment.
- Intrinsic Motivation: Interest found in the action itself.
Example:
Creating a reward system is not always the best way to foster user engagement.
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Normalcy Bias
Definition:
We often underestimate the likelihood of negative events and their consequences. We tend to think that everything will work as it always has, and therefore, we minimize exceptional events.
Example:
In the pre-design phases, it's important to consider all possible use cases, even the less probable ones, to ensure that the product/service designed will be suitable for every situation.
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Survivorship Bias
Definition:
It's a form of selection that involves giving more weight to examples of success than failure. Attention is focused on the success of individuals or organizations, even though these cases are not representative.
Example:
In the analysis of user tests, there may be a tendency to focus on successful cases and minimize cases where participants failed.
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Outgroup Homogeneity Bias
Definition:
Members of an outgroup are perceived as much more similar to each other than members of the ingroup. There is a tendency to consider their physical appearance, personality, and behaviors as more similar than they actually are. This reduction of differences in outgroups allows for simplified categorization but can lead to prejudice.
Outgroup: A social, cultural, etc., group to which an individual does not belong.
Ingroup: A group to which an individual belongs.
Example:
When designing a business interface for professionals, it's important to consider the diversity of end users and not reduce them to their common characteristics.
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Illusory Correlation
Definition:
There is a tendency to see patterns or associations in random data. This comes from our tendency to perceive random data as more regular than it actually is.
Example:
In the analysis of quantitative data, one may expect to observe trends and correlations that may not actually exist. To avoid this bias, it's important to consider the sample size.