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 product ideas and assumptions with real customers to ensure they meet market needs. Essential for reducing risk and ensuring product-market fit.
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 extent to which a measure represents all facets of a given construct, ensuring the content covers all relevant aspects. Important for ensuring that assessments and content accurately reflect the intended subject matter.
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 testing method where the internal structure of the system is not known to the tester, focusing solely on input and output. Essential for validating the functionality of digital products from an end-user perspective.
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.
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 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.
A practice of performing testing activities in the production environment to monitor and validate the behavior and performance of software in real-world conditions. Crucial for ensuring the stability, reliability, and user satisfaction of digital products in a live environment.
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 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.
Plan-Do-Check-Act (PDCA) is an iterative four-step management method used for continuous improvement of processes and products. Essential for quality control and operational efficiency.
Research conducted to assess the effectiveness, usability, and impact of a design or product. Essential for validating design decisions and improving user experiences.
A research method used to determine how desirable a product or feature is to potential users. Crucial for understanding user preferences and guiding product development.
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.
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 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.
Also known as Expert Review, a method where experts assess a product or system against established criteria to identify usability issues and areas for improvement. Essential for leveraging expert insights to enhance product quality and usability.
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 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 quick and cost-effective usability testing method where feedback is gathered from users in informal settings, often in public places. Useful for gaining rapid insights into user behavior and improving designs iteratively.
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.
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 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.
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.
A strategic approach where multiple potential solutions are tested to identify the most promising one. Crucial for innovation and reducing risk in decision-making.
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 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 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.
Human in the Loop (HITL) integrates human judgment into the decision-making process of AI systems. Crucial for ensuring AI reliability and alignment with human values.
A principle that states the time it takes to make a decision increases with the number and complexity of choices available. Crucial for designing user interfaces that minimize cognitive load and enhance decision-making efficiency.
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.
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.
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
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.