The process of creating an early model of a product to test and validate ideas, features, and design choices before full-scale production. Essential for validating design choices and gathering user feedback early in the development process.
A method of creating and testing user interfaces using hand-drawn sketches and mockups on paper. Essential for early-stage design validation and gathering user feedback.
Minimum Viable Product (MVP) is a version of a product with just enough features to be usable by early customers who can then provide feedback for future product development. Essential for validating product ideas quickly and cost-effectively, allowing teams to learn about customer needs without fully developing the product.
The process of quickly creating a preliminary version of a product to test and validate ideas before full-scale development. Important for validating design concepts and gathering user feedback early.
Minimum Viable Experience (MVE) is the simplest version of a product that delivers a complete and satisfying user experience while meeting core user needs. Essential for rapidly validating product concepts and user experience designs while ensuring that even early versions of a product provide value and a positive impression to users.
A usability testing method where users interact with a system they believe to be autonomous, but which is actually operated by a human. Essential for testing concepts and interactions before full development.
A statistical method used to assess the generalizability of a model to unseen data, involving partitioning a dataset into subsets for training and validation. Essential for evaluating model performance and preventing overfitting in digital product analytics.
The process of testing and evaluating a design to ensure it meets user needs and business goals before final implementation. Crucial for ensuring that designs are effective and meet intended objectives.
The process of testing product ideas and assumptions with real customers to ensure they meet market needs. Essential for reducing risk and ensuring product-market fit.
Proof of Concept (PoC) is a demonstration, usually in the form of a prototype or pilot project, to verify that a concept or theory has practical potential. Crucial for validating ideas, demonstrating feasibility, and securing support for further development in product design and innovation processes.
A framework for discovering and validating the right market for a product, building the right product features, and validating the business model. Important for ensuring that products meet market needs and customer expectations.
A preliminary version of a project or system used to test and validate its feasibility before full-scale implementation. Crucial for identifying potential issues and making necessary adjustments to improve the final product.
A time-constrained, intensive process that helps teams quickly design, prototype, and test ideas. Important for rapidly developing and validating design solutions.
The process of running a system for an extended period to detect early failures and ensure reliability. Important for ensuring the stability and performance of digital products before full-scale deployment.
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 demonstration of the new features and functionalities of a product release, typically used to showcase progress and gather feedback before the official launch. Crucial for validating product features and gathering stakeholder feedback before a full release.
Minimum Viable Feature (MVF) is the smallest possible version of a feature that delivers value to users and allows for meaningful feedback collection. Crucial for rapid iteration in product development, enabling teams to validate ideas quickly and efficiently while minimizing resource investment.
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.
A method of testing two identical versions of a webpage or app to ensure the accuracy of the testing tool. Important for validating the effectiveness of A/B testing tools and processes.
The process of evaluating a product by testing it with real users to gather feedback and identify usability issues. Essential for validating design decisions and ensuring the product meets user needs.
User Acceptance Testing (UAT) is the final phase of the software testing process where actual users test the software to ensure it meets their requirements. Crucial for validating that the software functions correctly in real-world scenarios before its release.
A research approach that starts with a theory or hypothesis and uses data to test it, often moving from general to specific. Essential for validating theories and making informed decisions based on data.
Research conducted to assess the effectiveness, usability, and impact of a design or product. Essential for validating design decisions and improving user experiences.
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.
A preliminary testing phase conducted by internal staff to identify bugs before releasing the product to external testers or customers. Crucial for ensuring product quality and functionality before broader release.
The process of determining whether there is a need or demand for a product in the target market, often through testing and feedback. Crucial for ensuring that a product will meet market needs and be successful.
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 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-Based Systems Engineering (MBSE) is a methodology that uses visual modeling to support system requirements, design, analysis, and validation activities throughout the development lifecycle. Essential for managing complex systems, improving communication among stakeholders, and enhancing the overall quality and efficiency of systems engineering processes.
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.
User-Centered Design (UCD) is an iterative design approach that focuses on understanding users' needs, preferences, and limitations throughout the design process. Crucial for creating products that are intuitive, efficient, and satisfying for the intended users.
A strategic approach where multiple potential solutions are tested to identify the most promising one. Crucial for innovation and reducing risk in decision-making.
An environment used for testing software to identify issues and ensure quality before production deployment. Important for detecting and fixing bugs to ensure the software's reliability and performance.
A research method in which participants interact with a series of potential product concepts in quick succession, providing rapid feedback on multiple ideas. Useful for quickly gathering user feedback on various concepts and iterating based on their preferences.
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.
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.
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 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 technique used to evaluate a product or system by testing it with real users to identify any usability issues and gather qualitative and quantitative data on their interactions. Crucial for identifying and resolving usability issues to improve user satisfaction and performance.
A meeting at the end of a sprint where the development team presents their completed work to stakeholders. Crucial for gathering feedback and demonstrating progress.
A Gestalt principle that states that objects that are similar in appearance are perceived as being more related than objects that are dissimilar. Essential for creating visually cohesive and intuitive designs.
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.
A problem-solving approach that involves breaking down complex problems into their most basic, foundational elements. Crucial for developing innovative solutions by understanding and addressing core issues.
A technology and research method that measures where and how long a person looks at various areas on a screen or interface. Crucial for understanding user attention and improving interface design.
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.
Minimum Marketable Feature (MMF) is the smallest set of functionality that delivers significant value to users and can be marketed effectively. Crucial for prioritizing development efforts and releasing valuable product increments quickly, balancing user needs with business objectives.