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 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.
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.
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.
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.
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.
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.
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.
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.
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.
Statistical data relating to a particular population and groups within it. Crucial for market research and understanding target audiences.
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 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 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.
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.
A statistical rule stating that nearly all values in a normal distribution (99.7%) lie within three standard deviations (sigma) of the mean. Important for identifying outliers and understanding variability in data, aiding in quality control and performance assessment in digital product design.
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.
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 statistical method that models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. Essential for predicting outcomes and understanding relationships between variables in digital product design and analysis.
The use of statistical techniques and algorithms to analyze historical data and make predictions about future outcomes. Important for optimizing marketing strategies and anticipating customer needs.
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.
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.
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.
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 making predictions about future trends based on current and historical data. Useful for anticipating user needs and market trends to inform design decisions.
An experimental design where different groups of participants are exposed to different conditions, allowing for comparison between groups. Important for understanding and applying different experimental designs in user research.
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 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.
The interpretation of historical data to identify trends and patterns. Important for understanding past performance and informing future 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 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 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 data-driven methodology aimed at improving processes by identifying and removing defects, and reducing variability. Crucial for enhancing the quality and efficiency of digital product development processes.
A bias that occurs when researchers' expectations influence the outcome of a study. Crucial for designing research methods that ensure objectivity and reliability.
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 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 process of examining large and varied data sets to uncover hidden patterns, correlations, and insights. Important for making informed business decisions and identifying opportunities for innovation and growth.
A method of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.
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 practice of using data analytics and metrics to make informed decisions, focusing on measurable outcomes and efficiency rather than intuition or traditional methods. Important for optimizing design processes, improving product performance, and making data-driven decisions that enhance user experience and business success.