The degree to which a product or system can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use. Essential for creating products that are easy to use and meet user needs effectively.
A usability inspection method where experts review a user interface against a set of heuristics to identify usability issues. Crucial for identifying usability problems early in the design process.
A behavioral economic theory that describes how people choose between probabilistic alternatives that involve risk, where the probabilities of outcomes are known. Crucial for understanding decision-making under risk and designing systems that align with user behavior.
A systematic evaluation of all features in a product to determine their usage, effectiveness, and alignment with business goals. Essential for optimizing product performance and user satisfaction.
The process of evaluating and categorizing potential customers based on their likelihood to purchase. Essential for prioritizing sales efforts and improving conversion rates.
A systematic process for determining and addressing needs or gaps between current conditions and desired outcomes. Important for identifying user requirements and guiding the development of digital products that meet those needs.
The process of evaluating the impact and success of a feature after its release, based on predefined metrics and user feedback. Crucial for understanding the effectiveness of features and informing future development.
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 tendency to believe that large or significant events must have large or significant causes. Important for understanding cognitive biases in decision-making and designing systems that present accurate causal relationships.
Know Your Customer (KYC) is a process used by businesses to verify the identity of their clients and assess potential risks of illegal intentions for the business relationship. Essential for preventing fraud, money laundering, and terrorist financing, particularly in financial services, while also ensuring compliance with regulatory requirements and building trust with customers.
The assessment of the strengths and weaknesses of current and potential competitors to identify competitive advantages and disadvantages. Essential for strategic planning and positioning within the market.
The process by which a measure or metric comes to replace the underlying objective it is intended to represent, leading to distorted decision-making. Important for ensuring that metrics accurately reflect true objectives and designing systems that prevent metric manipulation.
The principle that the more a metric is used to make decisions, the more it will be subject to corruption and distort the processes it is intended to monitor. Important for understanding the limitations and potential distortions of metrics in design and evaluation.
Retrieval-Augmented Generation (RAG) is an AI approach that combines retrieval of relevant documents with generative models to produce accurate and contextually relevant responses. Essential for improving the accuracy and reliability of AI-generated content.
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.
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.
A specific organization of colors, which helps in the representation of color in both physical and digital forms. Crucial for accurate color representation and consistency across different mediums.
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 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.
The process by which search engines organize and store web content to facilitate fast and accurate information retrieval. Crucial for understanding how search engines work and ensuring that web content is accessible and searchable.
A cognitive bias where individuals tend to avoid risks when they perceive potential losses more acutely than potential gains. Important for understanding decision-making behavior in users and designing systems that mitigate risk aversion.
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.
A prioritization framework used to assess and compare the value a feature will deliver to users against the complexity and cost of implementing it. Crucial for making informed decisions about feature prioritization and resource allocation.
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 judge the likelihood of an event based on its relative size rather than absolute probability. Important for understanding user decision-making biases and designing systems that present information accurately.
The tendency for individuals to present themselves in a favorable light by overreporting good behavior and underreporting bad behavior in surveys or research. Crucial for designing research methods that mitigate biases and obtain accurate data.
An estimation technique used in Agile software development where team members assign story points to tasks through consensus-based discussion. Essential for collaborative and accurate project planning and estimation.
The risk that users will find the product difficult or confusing to use, preventing them from effectively utilizing its features. Crucial for making sure the product is user-friendly and intuitive, enhancing the user experience and adoption.
A relative estimation technique used in Agile project management to quickly assess the size and complexity of tasks by assigning them T-shirt sizes (e.g., small, medium, large). Crucial for efficient project planning and workload management.
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.
The process of designing and refining prompts to elicit accurate and relevant responses from AI models. Crucial for optimizing the performance of AI applications.
The persistence of misinformation in memory and influence on reasoning, even after it has been corrected. Crucial for understanding and mitigating the impact of misinformation in design and communication.
A cognitive bias where people attribute group behavior to the characteristics of the group members rather than the situation. Crucial for understanding team dynamics and avoiding misattribution in collaborative settings.
Garbage In-Garbage Out (GIGO) is a principle stating that the quality of output is determined by the quality of the input, especially in computing and data processing. Crucial for ensuring accurate and reliable data inputs in design and decision-making processes.
The degree to which a product satisfies strong market demand, often considered a key indicator of a product's potential for success. Essential for validating the viability of a product in the market and guiding strategic decisions.
A logical fallacy that occurs when one assumes that what is true for a part is also true for the whole. Important for avoiding incorrect assumptions in design and decision-making.
The phenomenon where taking a test on material improves long-term retention of that material more than additional study sessions. Crucial for designing educational tools and methods that enhance learning and retention.
The tendency to recall past behavior in a way that aligns with current beliefs and attitudes. Crucial for understanding how memories and self-perception can be influenced by current perspectives.
Total Addressable Market (TAM) represents the total revenue opportunity available if a product or service achieves 100% market share. Essential for understanding the full potential of a market.
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 metric that measures how engaged users are with a product, often based on usage frequency, feature adoption, and user feedback. Crucial for assessing user satisfaction and identifying areas for improvement in the product experience.
A cognitive bias where people wrongly believe they have direct insight into the origins of their mental states, while treating others' introspections as unreliable. Important for designing experiences that account for discrepancies between user self-perception and actual behavior.
Obstacles that make it difficult for new competitors to enter an industry, such as high capital requirements, strong brand loyalty, or regulatory hurdles. Crucial for assessing the competitive landscape and the feasibility of entering a new market.
The collection of all the backlinks (inbound links) pointing to a website, used to assess its authority and influence in search engine rankings. Essential for understanding and improving SEO strategies.
In AI and machine learning, a prompt that specifies what should be avoided or excluded in the generated output, guiding the system to produce more accurate and relevant results. Crucial for refining AI-generated content by providing clear instructions on undesired elements, improving output quality and relevance.
The use of data from digital devices to measure and understand individual behavior and health patterns. Crucial for developing personalized user experiences and health interventions.
A cognitive bias where individuals overestimate the likelihood of extreme events regressing to the mean. Crucial for understanding decision-making and judgment under uncertainty.
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 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.
Monthly Recurring Revenue (MRR) is a metric that quantifies the predictable revenue generated each month from customers. This metric is crucial for SaaS companies to track financial health and growth.
The process of identifying, assessing, and controlling dependencies between tasks or projects to minimize risks and ensure smooth project execution. Crucial for effective project management and delivery.
Serviceable Obtainable Market (SOM) is the portion of the Serviceable Addressable Market that a company can realistically capture. Essential for setting achievable sales and market share goals.
Measurements used to evaluate the success of an organization, employee, or process in meeting goals. Necessary for assessing performance and driving continuous improvement.
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 technique used in agile project management where tasks are estimated using the Fibonacci sequence to reflect the uncertainty and complexity of work. Essential for accurate and realistic task estimation in agile methodologies.
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.
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing. Important for understanding and mitigating errors in AI systems.
A method used in AI and machine learning to ensure prompts and inputs are designed to produce the desired outcomes. Essential for improving the accuracy and relevance of AI responses.
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 where a person's subjective confidence in their judgments is greater than their objective accuracy. Crucial for understanding user decision-making and designing systems that account for overconfidence.