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.
The series of actions or operations involved in the acquisition, interpretation, storage, and retrieval of information. Crucial for understanding how users handle information and designing systems that align with cognitive processes.
The process of creating visual representations of data or information to enhance understanding and decision-making. Essential for organizing information and making complex data accessible.
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.
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.
The way information is presented to users, which can significantly influence their decisions and perceptions. Important for designing messages and interfaces that guide user choices effectively.
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.
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.
A search method that seeks to improve search accuracy by understanding the contextual meaning of terms in a query rather than just matching keywords. Important for understanding modern search algorithms and optimizing content accordingly.
Obstacles to effective communication that arise from differences in understanding the meanings of words and symbols used by the communicators. Crucial for designing clear and effective communication systems and avoiding misunderstandings.
The representation of data through graphical elements like charts, graphs, and maps to facilitate understanding and insights. Essential for making complex data accessible and actionable for users.
The ability to identify and interpret patterns in data, often used in machine learning and cognitive psychology. Crucial for designing systems that leverage pattern recognition for predictive analytics and user interactions.
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 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 statistical phenomenon where a large number of hypotheses are tested, increasing the chance of a rare event being observed. Crucial for understanding and avoiding false positives in data analysis.
Qualitative data that provides insights into the context and human aspects behind quantitative data. Crucial for gaining deep insights into user behaviors and motivations.
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 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.
Metrics that may look impressive but do not provide meaningful insights into the success or performance of a product or business, such as total page views or social media likes. Important for distinguishing between metrics that drive real business value and those that do not.
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.
Technologies that enable machines to understand and interpret data on the web in a human-like manner, enhancing connectivity and usability of information. Essential for improving data interoperability and accessibility on the web.
Code added to a webpage to help search engines understand the content and provide more informative results for users, enhancing SEO. Essential for improving SEO and ensuring that search engines can accurately interpret webpage content.
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.
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 statistical method used to identify underlying relationships between variables by grouping them into factors. Crucial for simplifying data and identifying key variables in research.
The interpretation of historical data to identify trends and patterns. Important for understanding past performance and informing future decision-making.
The process of using statistical analysis and modeling to explore and interpret business data to make informed decisions. Essential for improving business performance, identifying opportunities for growth, and driving strategic planning.
A graphical representation of the distribution of numerical data, typically showing the frequency of data points in successive intervals. Important for analyzing and interpreting data distributions, aiding in decision-making and optimization in product design.
The process of linking language to its real-world context in AI systems, ensuring accurate understanding and interpretation. Crucial for improving the relevance and accuracy of AI-generated responses.
A set of principles describing how the human mind organizes visual information into meaningful wholes. Crucial for designing intuitive digital interfaces and cohesive user experiences that align with natural human perception patterns.
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 practice of measuring and analyzing data about digital product adoption, usage, and performance to inform business decisions. Crucial for making data-driven decisions that improve product performance and user satisfaction.
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 research method that focuses on collecting and analyzing numerical data to identify patterns, relationships, and trends, often using surveys or experiments. Essential for making data-driven decisions and validating hypotheses with statistical evidence.
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.
Perceivable, Operable, Understandable, and Robust (POUR) are the four main principles of web accessibility. These principles are essential for creating inclusive digital experiences that can be accessed and used by people with a wide range of abilities and disabilities.
The tendency to perceive and interpret information based on prior experiences and expectations, influencing how different users perceive design differently. Important for designing interfaces that meet user expectations, improving usability and intuitive navigation.
A statistical distribution where most occurrences take place near the mean, and fewer occurrences happen as you move further from the mean, forming a bell curve. Crucial for data analysis and understanding variability in user behavior and responses.
A statistical method used to predict a binary outcome based on prior observations, modeling the probability of an event as a function of independent variables. Essential for predicting categorical outcomes in digital product analysis and user behavior modeling.
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 Gestalt principle that describes the visual relationship between a figure and its background, crucial for understanding visual perception. Important for designing clear and effective visual hierarchies in user interfaces.
An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Essential for driving data-informed decision making, predicting trends, and uncovering valuable insights in digital product design and development.
Specific roles assigned to HTML elements to define their purpose and behavior in an accessible manner. Crucial for improving the accessibility and usability of web applications.
ARIA attributes that define additional characteristics of elements, such as roles and relationships. Important for enhancing the accessibility and usability of web applications.
The systematic computational analysis of data or statistics to understand and improve business performance. Essential for data-driven decision making in design, product management, and marketing.
Voice User Interface (VUI) is a system that allows users to interact with a device or software using voice commands. Essential for creating hands-free, intuitive user experiences.
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.
A Gestalt principle that states that objects that are similar in appearance are perceived as being more related than objects that are dissimilar. Essential for creating visually cohesive and intuitive designs.
A comprehensive analysis of a website to assess its performance in search engine rankings and identify areas for improvement. Essential for diagnosing and enhancing a website's SEO performance.
Interference in the communication process caused by ambiguity in the meaning of words and phrases, leading to misunderstandings. Crucial for designing clear communication channels and reducing misunderstandings in user 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.
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 theory that emphasizes the role of emotions in risk perception and decision-making, where feelings about risk often diverge from cognitive assessments. Important for designing systems that account for emotional responses to risk and improve decision-making.
The study of cultural norms, values, and practices and their influence on human behavior. Useful for designing products that are culturally sensitive and relevant.
Quantitative measures used to track and assess the performance and success of a product, such as usage rates, customer satisfaction, and revenue. Essential for making data-driven decisions to improve product performance and achieve business goals.
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 form of regression analysis where the relationship between the independent variable and the dependent variable is modeled as an nth degree polynomial. Useful for modeling non-linear relationships in digital product data analysis.
The study of narrative and narrative structure and the ways that these affect our perception. Useful for understanding and applying narrative techniques in design and communication.
The study of the principles and techniques of art, including visual composition, aesthetics, and the role of art in society. Important for understanding visual design principles and enhancing the aesthetic quality of digital products.
Model-View-Controller (MVC) is an architectural pattern that separates an application into three main logical components: the Model (data), the View (user interface), and the Controller (processes that handle input). Essential for creating modular, maintainable, and scalable software applications by promoting separation of concerns.