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.
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.
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.
An experimental design where subjects are paired based on certain characteristics, and then one is assigned to the treatment and the other to the control group. Important for reducing variability and improving the accuracy of experimental results.
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.
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 research method that involves repeated observations of the same variables over a period of time. Crucial for understanding changes and developments over time.
A research method that involves observing subjects in their natural environment. Crucial for gathering authentic data and insights into real-world behaviors and interactions.
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.
The extent to which a measure represents all facets of a given construct, ensuring the content covers all relevant aspects. Important for ensuring that assessments and content accurately reflect the intended subject matter.
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 research design where the same participants are used in all conditions of an experiment, allowing for the comparison of different conditions within the same group. Essential for reducing variability and improving the reliability of experimental results.
A technology and research method that measures where and how long a person looks at various areas on a screen or interface. Crucial for understanding user attention and improving interface design.
A qualitative research method involving direct conversations with users to gather insights into their needs, behaviors, and experiences. Essential for gaining deep insights into user perspectives and informing design decisions.
A model predicting the speed-accuracy trade-off in pointing tasks when using devices like a mouse, important for user interface design. Useful for designing user interfaces that are efficient and easy to navigate.
A cognitive bias where a person's subjective confidence in their judgments is greater than their objective accuracy. Crucial for understanding user decision-making and designing systems that account for overconfidence.
A cognitive bias where individuals overestimate the accuracy of their judgments, especially when they have a lot of information. Important for understanding and mitigating overconfidence in user decision-making.
Data points that differ significantly from other observations and may indicate variability in a measurement, experimental errors, or novelty. Crucial for identifying anomalies and ensuring the accuracy and reliability of data in digital product design.
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.
Retrieval-Augmented Generation (RAG) is an AI approach that combines retrieval of relevant documents with generative models to produce accurate and contextually relevant responses. Essential for improving the accuracy and reliability of AI-generated content.
A structured communication technique originally developed as a systematic, interactive forecasting method which relies on a panel of experts. Important for gathering expert opinions and making informed decisions.
A method in natural language processing where multiple prompts are linked to generate more complex and contextually accurate responses. Essential for enhancing the capability and accuracy of AI models in digital products that rely on natural language understanding.
A method used in AI and machine learning to ensure prompts and inputs are designed to produce the desired outcomes. Essential for improving the accuracy and relevance of AI responses.
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 process by which a measure or metric comes to replace the underlying objective it is intended to represent, leading to distorted decision-making. Important for ensuring that metrics accurately reflect true objectives and designing systems that prevent metric manipulation.
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 individuals with low ability at a task overestimate their ability, while experts underestimate their competence. Crucial for designers to create educational content and user interfaces that accommodate varying levels of user expertise.
The process of understanding user behaviors, needs, and motivations through various qualitative and quantitative methods. Essential for designing user-centered products and ensuring they meet actual user needs.
A range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter. Essential for making inferences about population parameters and understanding the precision of estimates in product design analysis.
A cognitive bias where the total probability assigned to a set of events is less than the sum of the probabilities assigned to each event individually. Important for understanding how users estimate probabilities and make decisions under uncertainty.
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing. Important for understanding and mitigating errors in AI systems.
A type of model architecture primarily used in natural language processing tasks, known for its efficiency and scalability. Essential for state-of-the-art NLP applications.
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 see patterns in random data. Important for designers to improve data interpretation and avoid false conclusions based on perceived random patterns.
A cognitive bias where people give greater weight to outcomes that are certain compared to those that are merely probable. Important for designers to consider how users weigh certain outcomes more heavily in their decision-making.
A statistical theory that states that the distribution of sample means approximates a normal distribution as the sample size becomes larger, regardless of the population's distribution. Important for making inferences about population parameters and ensuring the validity of statistical tests in digital product design.
Case-Based Reasoning (CBR) is an AI method that solves new problems based on the solutions of similar past problems. This approach is essential for developing intelligent systems that learn from past experiences to improve problem-solving capabilities.
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.
Generative Pre-trained Transformer (GPT) is a type of AI model that uses deep learning to generate human-like text based on given input. This technology is essential for automating content creation and enhancing interactive experiences.
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
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 fictional character created to represent a user type that might use a site, brand, or product in a similar way, guiding design decisions. Essential for user-centered design, ensuring that products meet the needs of target 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 component in neural networks that allows the model to focus on specific parts of the input, improving performance. Essential for developing advanced AI models, particularly in natural language processing.
The perception of a relationship between two variables when no such relationship exists. Crucial for understanding and avoiding biases in data interpretation and decision-making.
The use of natural language processing to identify and extract subjective information from text, determining the sentiment expressed. Crucial for understanding public opinion and customer feedback.
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
Also known as the 68-95-99.7 Rule, it states that for a normal distribution, nearly all data will fall within three standard deviations of the mean. Important for understanding the distribution of data and making predictions about data behavior in digital product design.
The process of predicting future customer demand using historical data and other information. Crucial for optimizing inventory levels, production schedules, and supply chain management.
A cognitive bias where people remember scenes as being more expansive than they actually were. Important for understanding how users perceive and recall visual information, aiding in better visual design decisions.
Knowledge Organization System (KOS) refers to a structured framework for organizing, managing, and retrieving information within a specific domain or across multiple domains. Essential for improving information findability, enhancing semantic interoperability, and supporting effective knowledge management in digital environments.
The study of strategic decision making, incorporating psychological insights into traditional game theory models. Useful for understanding complex user interactions and designing systems that account for strategic behavior.
Internet of Things (IoT) refers to a network of interconnected physical devices embedded with electronics, software, sensors, and network connectivity, enabling them to collect and exchange data. Essential for creating smart, responsive environments and improving efficiency across various industries by enabling real-time monitoring, analysis, and automation.
A cognitive bias where repeated statements are more likely to be perceived as true, regardless of their actual accuracy. Crucial for understanding how repetition influences beliefs and designing communication strategies for users.
A cognitive bias where individuals overestimate their own abilities, qualities, or performance relative to others. Important for understanding user self-perception and designing systems that account for inflated self-assessments.