Quantitative data that provides broad, numerical insights but often lacks the contextual depth that thick data provides. Useful for capturing high-level trends and patterns, but should be complemented with thick data to gain a deeper understanding of user behavior and motivations.
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 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 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 statistical technique that uses several explanatory variables to predict the outcome of a response variable, extending simple linear regression to include multiple input variables. Crucial for analyzing complex relationships in digital product data.
Information Visualization (InfoVis) is the study and practice of visual representations of abstract data to reinforce human cognition. Crucial for transforming complex data into intuitive visual formats, enabling faster insights and better decision-making.
The error of making decisions based solely on quantitative observations and ignoring all other factors. Important for ensuring a holistic approach to 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.
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.
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.
Total Quality Management (TQM) is a comprehensive management approach focused on continuous improvement in all aspects of an organization. Essential for ensuring high-quality products and services and achieving customer satisfaction.
A visual tool that maps out opportunities and the corresponding solutions, helping teams identify and prioritize where to focus their efforts. Crucial for strategic planning and ensuring alignment between problems and solutions.