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 statistical phenomenon where two independent events appear to be correlated due to a selection bias. Important for accurately interpreting data and avoiding misleading conclusions.
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 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.
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 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.
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.
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.
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.
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 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.
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.
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.
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 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.
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 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.
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 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 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.
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.
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.
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 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 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 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 data and insights to understand and manage relationships with customers and prospects. Crucial for enhancing customer engagement and building stronger relationships.
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 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 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.
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 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.
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.
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 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.
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.
Artificial Intelligence of Things (AIoT) is the integration of AI with the Internet of Things (IoT) to create smart systems that can learn and adapt. Crucial for developing advanced, intelligent products that offer enhanced user experiences and operational efficiencies.
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 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.
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.
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.
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 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 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.
A cognitive bias where people perceive past events as having been more predictable than they actually were. Important for understanding and mitigating biases in user feedback and decision-making.
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.
The percentage of users who continue to use a product or service over a specified period, indicating user loyalty and engagement. Essential for assessing the effectiveness of user retention strategies and improving user experience.
A machine learning-based search engine algorithm used by Google to help process search queries and provide more relevant results. Important for understanding modern SEO practices and how search engines interpret and rank web content.
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
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.
Natural Language Processing (NLP) is a field of AI focused on the interaction between computers and humans using natural language. Essential for developing applications like chatbots, language translation, and sentiment analysis.
The percentage of visitors to a website who navigate away from the site after viewing only one page. Important for understanding user engagement and the effectiveness of a website's content and design.