A type of bias that occurs when the observer's expectations or beliefs influence their interpretation of what they are observing, including experimental outcomes. Essential for ensuring the accuracy and reliability of research and data collection.
The tendency to favor people who are similar to oneself in terms of background, beliefs, or interests. Important for recognizing and mitigating bias in user research and team dynamics.
A bias that occurs when researchers' expectations influence the outcome of a study. Crucial for designing research methods that ensure objectivity and reliability.
The tendency for individuals to present themselves in a favorable light by overreporting good behavior and underreporting bad behavior in surveys or research. Crucial for designing research methods that mitigate biases and obtain accurate data.
A bias that occurs when the sample chosen for a study or survey is not representative of the population being studied, affecting the validity of the results. Important for ensuring the accuracy and reliability of research findings and avoiding skewed data.
A tendency for respondents to answer questions in a manner that is not truthful or accurate, often influenced by social desirability or survey design. Important for understanding and mitigating biases in survey and research data.
A cognitive bias where people assume others share the same beliefs, values, or preferences as themselves. Important for helping designers avoid projecting their own biases and assumptions onto users during research and design.
A statistical phenomenon where two independent events appear to be correlated due to a selection bias. Important for accurately interpreting data and avoiding misleading conclusions.
The tendency for individuals to give positive responses or feedback out of politeness, regardless of their true feelings. Crucial for obtaining honest and accurate user feedback.
A cognitive bias where individuals favor others who are perceived to be similar to themselves, affecting judgments and decision-making. Crucial for understanding biases in team dynamics and decision-making processes among designers.
A cognitive bias that occurs when conclusions are drawn from a non-representative sample, focusing only on successful cases and ignoring failures. Crucial for making accurate assessments and designing systems that consider both successes and failures.
A cognitive bias where people perceive past events as having been more predictable than they actually were. Important for understanding and mitigating biases in user feedback and decision-making.
The tendency to judge the strength of arguments based on the believability of their conclusions rather than the logical strength of the arguments. Important for understanding cognitive biases that affect decision-making and user perceptions.
A cognitive bias where people overestimate the importance of information that is readily available. Essential for designers to understand and mitigate how easily accessible information can disproportionately influence decisions.
The tendency to search for, interpret, and remember information in a way that confirms one's preexisting beliefs or hypotheses. Crucial for understanding cognitive biases that affect user decision-making and designing interventions to mitigate them.
A cognitive bias where individuals underestimate their own abilities and performance relative to others, believing they are worse than average. Important for understanding self-perception biases among designers and designing systems that support accurate self-assessment.
A cognitive bias where decision-making is affected by the lack of information or uncertainty. Important for understanding and mitigating user decision-making biases due to uncertainty or lack of information.
A cognitive bias where people ignore the relevance of sample size in making judgments, often leading to erroneous conclusions. Crucial for designers to account for appropriate sample sizes in research and analysis.
A cognitive bias where individuals believe that past random events affect the probabilities of future random events. Important for designers to understand user decision-making biases related to randomness.
A cognitive bias where people ignore general statistical information in favor of specific information. Critical for designers to use general statistical information to improve decision-making accuracy and avoid bias.
A cognitive bias where individuals strengthen their beliefs when presented with evidence that contradicts them. Important for understanding user resistance to change and designing strategies to address and mitigate this bias.
A cognitive bias where people tend to believe that others are more affected by media messages and persuasive communications than they are themselves. Important for understanding media influence and designing communications that account for this bias in user perception.
The experience of noticing something for the first time and then frequently encountering it shortly after, also known as frequency illusion. Important for understanding user perception and cognitive biases in information processing.
A cognitive bias where one negative trait of a person or thing influences the perception of other traits. Important for designing experiences that counteract or mitigate negative biases in user perception.
Anchoring (also known as Focalism) is a cognitive bias where individuals rely heavily on the first piece of information (the "anchor") when making decisions. Crucial for understanding and mitigating initial information's impact on user decision-making processes.
A logical fallacy in which it is assumed that qualities of one thing are inherently qualities of another, due to an irrelevant association. Important for avoiding incorrect associations in user research and data interpretation.
A cognitive bias where people judge the likelihood of an event based on its relative size rather than absolute probability. Important for understanding user decision-making biases and designing systems that present information accurately.
A cognitive bias where people judge harmful actions as worse, or less moral, than equally harmful omissions (inactions). Important for understanding user decision-making and designing systems that mitigate this bias.
Systematic errors in AI models that arise from the data or algorithms used, leading to poor outcomes. Important for ensuring fairness and accuracy in AI systems.
The tendency to believe that large or significant events must have large or significant causes. Important for understanding cognitive biases in decision-making and designing systems that present accurate causal relationships.
The tendency to attribute intentional actions to others' behaviors, often overestimating their intent. Important for understanding and mitigating biases in user interactions and feedback.
A cognitive bias that causes people to overestimate the likelihood of negative outcomes. Important for understanding user risk perception and designing systems that address irrational pessimism.
A cognitive bias where people prefer the option that seems to eliminate risk entirely, even if another option offers a greater overall benefit. Important for understanding decision-making and designing risk communication for users.
The tendency for people to believe that others are telling the truth, leading to a general assumption of honesty in communication. Important for understanding communication dynamics and designing systems that account for this bias.
A cognitive bias where people overemphasize information that is placed prominently or in a way that catches their attention first. Crucial for designing interfaces and information displays that manage user attention effectively.
A cognitive bias where people rely too heavily on their own perspective and experiences when making decisions. Important for designers to recognize and mitigate their own perspectives influencing design decisions.
A cognitive bias where people favor members of their own group over those in other groups. Important for designing inclusive and equitable experiences for users.
A cognitive bias where people judge the likelihood of an event based on the size of its category rather than its actual probability. Crucial for designers to understand how category size influences user perception and decision-making processes.
A cognitive bias where people focus on the most noticeable or prominent information while ignoring less conspicuous details. Important for understanding user decision-making and ensuring balanced presentation of information.
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
A cognitive bias that causes people to attribute their own actions to situational factors while attributing others' actions to their character. Essential for helping designers recognize their own situational influences on interpreting user behavior and feedback.
A cognitive bias where individuals give stronger weight to payoffs that are closer to the present time compared to those in the future. Important for understanding user time-related decision-making and designing systems that encourage long-term thinking.
The tendency to overestimate the duration or intensity of the emotional impact of future events. Important for understanding user expectations and satisfaction.
A cognitive bias where individuals evaluate the value of bundled items differently than they would if the items were evaluated separately. Important for understanding user behavior and designing effective product bundles and pricing strategies.
A cognitive bias where individuals overestimate their ability to control impulsive behavior, leading to overexposure to temptations. Important for designing systems that help users manage self-control and avoid overexposure to temptations.
A cognitive bias where people prefer a greater variety of options when making simultaneous choices compared to sequential choices. Important for designers to consider user preferences for variety when designing choice architectures and product offerings.
The tendency to overestimate how much our future preferences and behaviors will align with our current preferences and behaviors. Important for understanding user behavior and designing experiences that account for changes over time.
The tendency to attribute positive qualities to one's own choices and downplay the negatives, enhancing post-decision satisfaction. Useful for understanding user satisfaction and designing experiences that reinforce positive decision outcomes.
A cognitive bias where people seek out more information than is needed to make a decision, often leading to analysis paralysis. Crucial for designing decision-making processes that avoid information overload for users.
A cognitive bias that leads individuals to prefer things to remain the same rather than change, often resisting new options or changes. Crucial for understanding resistance to change and designing strategies to overcome it among users.
The tendency to perceive a greater quantity as a better value, regardless of the actual utility. Important for understanding consumer behavior and designing effective marketing strategies.
The tendency for people's perception to be affected by their recurring thoughts at the time. Important for understanding how current thoughts influence user perception and decision-making.
The tendency to recall past behavior in a way that aligns with current beliefs and attitudes. Crucial for understanding how memories and self-perception can be influenced by current perspectives.
A cognitive bias where individuals interpret others' behaviors as having hostile intent, even when the behavior is ambiguous or benign. Important for understanding user interactions and designing experiences that mitigate negative interpretations.
The tendency to give more weight to negative experiences or information than positive ones. Crucial for understanding user behavior and designing systems that balance positive and negative feedback.
The tendency for individuals to recall information that is consistent with their current mood. Important for understanding how mood affects memory and designing experiences that account for emotional states.
A cognitive bias where people wrongly believe they have direct insight into the origins of their mental states, while treating others' introspections as unreliable. Important for designing experiences that account for discrepancies between user self-perception and actual behavior.
A cognitive bias where people perceive an outcome as certain while it is actually uncertain, based on how information is presented. Crucial for understanding and mitigating biased user decision-making.
Representativeness is a heuristic in decision-making where individuals judge the probability of an event based on how much it resembles a typical case. Crucial for understanding biases in human judgment and improving decision-making processes.
A cognitive bias where people attribute group behavior to the characteristics of the group members rather than the situation. Crucial for understanding team dynamics and avoiding misattribution in collaborative settings.