A symmetrical, bell-shaped distribution of data where most observations cluster around the mean. Fundamental in statistics and crucial for many analytical techniques used in digital product design and data-driven decision making.
An analysis comparing the costs and benefits of a decision or project to determine its feasibility and value. Important for making informed business and design decisions.
The error of making decisions based solely on quantitative observations and ignoring all other factors. Important for ensuring a holistic approach to decision-making.
The objective analysis and evaluation of an issue in order to form a judgment. Essential for making informed and rational design decisions.
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.
A statistical measure that quantifies the amount of variation or dispersion of a set of data values. Essential for understanding data spread and variability, which helps in making informed decisions in product design and analysis.
The study of how individuals make choices among alternatives and the principles that guide these choices. Important for designing decision-making processes and interfaces that help users make informed choices.
Strengths, Weaknesses, Opportunities, and Threats (SWOT) is a strategic planning tool that is applied to a business or project. Essential for strategic planning and decision-making.
A tree-like model of decisions and their possible consequences, used in data mining and machine learning for both classification and regression tasks. Valuable for creating interpretable models in digital product design and user behavior analysis.
The value or satisfaction derived from a decision, influencing the choices people make. Crucial for understanding user preferences and designing experiences that maximize satisfaction.
A decision-making paradox that shows people's preferences can violate the expected utility theory, highlighting irrational behavior. Important for understanding inconsistencies in user decision-making and designing better user experiences.
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 psychological phenomenon where the desire for harmony and conformity in a group results in irrational or dysfunctional decision-making. Crucial for recognizing and mitigating the risks of poor decision-making in teams.
A mental shortcut that relies on immediate examples that come to mind when evaluating a specific topic, concept, method, or decision. Crucial for understanding how people make decisions and the biases that influence their choices.
The study of psychology as it relates to the economic decision-making processes of individuals and institutions. Essential for understanding and influencing user decision-making and behavior in economic contexts.
The tendency for individuals to continue a behavior or endeavor as a result of previously invested resources (time, money, or effort) rather than future potential benefits. Important for understanding decision-making biases and designing systems that help users avoid irrational persistence.
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 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 theory in economics that models how rational individuals make decisions under risk by maximizing the expected utility of their choices. Essential for understanding decision-making under risk.
A mode of thinking, derived from Dual Process Theory, that is slow, deliberate, and analytical, requiring more cognitive effort and conscious reasoning. Crucial for designing complex tasks and interfaces that require thoughtful decision-making and problem-solving, ensuring they are clear and logical for users.
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 method used to create detailed narratives of potential future events to explore and understand possible outcomes and inform decision-making. Essential for strategic planning and anticipating the impact of different decisions or changes.
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.
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.
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.
The financial performance of a product, measured by its ability to generate revenue and profit relative to its costs and expenses. Important for assessing the financial success of a product and making informed business decisions.
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 research method that involves repeated observations of the same variables over a period of time. Crucial for understanding changes and developments over time.
Business Intelligence (BI) encompasses technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. Crucial for making data-driven decisions and improving business performance.
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.
The process of gathering and analyzing information about competitors to inform business strategy and decision-making. Essential for understanding market positioning and developing effective competitive strategies.
The interpretation of historical data to identify trends and patterns. Important for understanding past performance and informing future decision-making.
The ability to intuitively understand what makes a product successful, including market needs, user experience, and competitive landscape. Important for making informed decisions that lead to successful product development.
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.
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.
A digital replica of a physical entity, used to simulate, analyze, and optimize real-world operations. Essential for improving operational efficiency and decision-making.
A mathematical framework used to analyze strategic interactions where the outcomes depend on the actions of multiple decision-makers. Useful for designing systems and processes that involve competitive or cooperative interactions.
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.
The final interaction a customer has with a brand before making a purchase. Important for understanding which touchpoints drive conversions.
A decision-making tool that helps prioritize tasks or projects based on specific criteria, such as impact and effort. Essential for effective project management and resource allocation.
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.
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
A type of data visualization that uses dots to represent values for two different numeric variables, plotted along two axes. Essential for identifying relationships, patterns, and outliers in datasets used in digital product design and analysis.
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 spread and pattern of data values in a dataset, often visualized through graphs or statistical measures. Critical for understanding the characteristics of data and informing appropriate analysis techniques in digital product development.
A collaborative tool used to visualize what a user thinks, feels, says, and does to better understand their experiences and needs. Essential for gaining deep insights into user behavior and guiding design decisions.
A theory that explains how individuals determine the causes of behavior and events, including the distinction between internal and external attributions. Crucial for understanding user behavior and designing experiences that address both internal and external factors.
A moment of significant change in a process or system, where the direction of growth, performance, or trend shifts markedly. Important for recognizing critical transitions in design or business strategies, enabling timely adjustments and informed decision-making.
Data points that represent an individual's, team's, or company's performance in the sales process. Essential for tracking progress, identifying issues, and optimizing sales strategies.
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 strategic framework used to analyze the external macro-environmental factors affecting an organization: Political, Economic, Social, Technological, Environmental, and Legal. Essential for strategic planning and understanding market dynamics.
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 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.
The process of collecting, analyzing, and reporting aggregate data about which pages a website visitor visits and in what order. Essential for understanding user behavior and improving website navigation and content.
A decision-making strategy where individuals allocate resources proportionally to the probability of an outcome occurring, rather than optimizing the most likely outcome. Important for understanding decision-making behaviors and designing systems that guide better resource allocation.
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.
The phenomenon where people continue a failing course of action due to the amount of resources already invested. Important for recognizing and mitigating biased decision-making.
A cognitive bias where individuals or organizations continue to invest in a failing project or decision due to the amount of resources already committed. Important for designers to recognize and mitigate their own risks of continuing unsuccessful initiatives.
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 philosophy that emphasizes reason and logic as the primary sources of knowledge and truth. Useful for understanding the foundations of logical thinking and decision-making in design and development.