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.
Numeronym for the term "10,000 Concurrent Clients", the challenge of optimizing network software to handle ten thousand simultaneous client connections. Important for ensuring scalability and performance in high-demand scenarios.
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.
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.
An intermediary that gathers and provides information to users, typically in an online context. Important for helping users make informed decisions based on aggregated data.
Zero Moment of Truth (ZMOT) is a concept in marketing that refers to the point in the buying cycle when the consumer researches a product before the seller even knows they exist. Crucial for understanding consumer behavior and optimizing marketing strategies to influence decision-making at this early stage.
A writing and design principle that suggests that things grouped in threes are more satisfying, effective, and memorable for audiences. Important for creating impactful and memorable content and designs.
The principle that elements in a digital interface maintain consistent appearance, position, and behavior across different pages and states to help users maintain orientation and familiarity. Important for creating a stable and predictable user experience, reducing disorientation and enhancing usability.
Narrative descriptions of how users might interact with a product or system to achieve specific goals, used to inform design and development. Important for understanding user needs and ensuring the design supports their tasks and goals.
A design pattern that combines human and machine intelligence to enhance decision-making and problem-solving. Important for leveraging AI to support and amplify human capabilities.
ModelOps (Model Operations) is a set of practices for deploying, monitoring, and maintaining machine learning models in production environments. Crucial for ensuring the reliability, scalability, and performance of AI systems throughout their lifecycle, bridging the gap between model development and operational implementation.
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.
The tendency for people to defer purchasing decisions to a later time, often leading to procrastination. Important for understanding consumer behavior and optimizing sales strategies.