Illusion of Validity
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.
44 topics found for:
“decision-making accuracy”
Overconfidence Effect
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.
Delphi Method
A structured communication technique originally developed as a systematic, interactive forecasting method which relies on a panel of experts. Important for gathering expert opinions and making informed decisions.
Base Rate Fallacy
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.
Surrogation
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.
Ratio Bias
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.
Extension Neglect
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.
Certainty Effect
A cognitive bias where people give greater weight to outcomes that are certain compared to those that are merely probable. Important for designers to consider how users weigh certain outcomes more heavily in their decision-making.
Clustering Illusion
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.
Regressive Bias
A cognitive bias where individuals overestimate the likelihood of extreme events regressing to the mean. Crucial for understanding decision-making and judgment under uncertainty.
Expectation Bias
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
Pessimism Bias
A cognitive bias that causes people to overestimate the likelihood of negative outcomes. Important for understanding user risk perception and designing systems that address irrational pessimism.
CoD
Cost of Delay (CoD) is a metric that quantifies the economic impact of delaying a project, feature, or task. Important for making informed decisions about project prioritization and resource allocation.
Representativeness
Representativeness is a heuristic in decision-making where individuals judge the probability of an event based on how much it resembles a typical case. Crucial for understanding biases in human judgment and improving decision-making processes.
Category Size Bias
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.
Subadditivity Effect
A cognitive bias where the total probability assigned to a set of events is less than the sum of the probabilities assigned to each event individually. Important for understanding how users estimate probabilities and make decisions under uncertainty.
Behavioral Game Theory
The study of strategic decision making, incorporating psychological insights into traditional game theory models. Useful for understanding complex user interactions and designing systems that account for strategic behavior.
Machine Learning
A type of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed. Crucial for developing intelligent systems that can make data-driven decisions.
Illusory Correlation
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.
Friction
Elements in a process that cause resistance or slow down user actions, which can lead to frustration or be used intentionally to prevent errors and encourage deliberate actions. Important for recognizing both the negative impact of unnecessary delays and the positive use of intentional friction to enhance user decision-making and reduce errors.
Content Validity
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.
Knowledge Engineering
The process of integrating knowledge into computer systems to solve complex problems, often used in AI development. Important for developing intelligent systems that can perform complex tasks and support decision-making in digital products.
GIGO
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.
Association Fallacy
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.
ROI
Return on Investment (ROI) is a performance measure used to evaluate the efficiency or profitability of an investment or compare the efficiency of different investments. Crucial for assessing the financial effectiveness of business decisions, projects, or initiatives.
Outliers
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.
Selection Bias
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.
Observer Bias
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.
Confidence Interval
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.
5V’s of Big Data
Characteristics of big data defined as Volume, Velocity, Variety, Veracity, and Value. Important for understanding the complexities and potential of big data in driving business insights and innovation.
STEEPLE Framework
Social, Technological, Economic, Environmental, Political, Legal, and Ethical (STEEPLE) is an analysis tool that examines the factors influencing an organization. Crucial for comprehensive strategic planning and risk management in product design.
Sales Forecasting
The process of estimating future sales based on historical data, trends, and market analysis. Crucial for setting realistic sales targets and planning resources effectively.
IBP
Integrated Business Planning (IBP) is a process that aligns strategic, operational, and financial planning to optimize business performance. It ensures cohesive and efficient planning across all functions.
ERP
Enterprise Resource Planning (ERP) are integrated software systems that manage business processes across various departments, such as finance, HR, and supply chain. Essential for improving operational efficiency and providing a unified view of business operations.
Illusory Truth Effect
A cognitive bias where repeated statements are more likely to be perceived as true, regardless of their actual accuracy. Crucial for understanding how repetition influences beliefs and designing communication strategies for users.
Longitudinal Study
A research method that involves repeated observations of the same variables over a period of time. Crucial for understanding changes and developments over time.
JAD
Joint Application Development (JAD) is a collaborative approach to gathering requirements and designing solutions in software development projects. It facilitates rapid decision-making and consensus-building by bringing together key stakeholders, including users, developers, and project managers, in structured workshop sessions.
BPA
Business Process Automation (BPA) refers to the use of technology to automate complex business processes. Essential for streamlining operations, reducing manual effort, and increasing efficiency in recurring tasks.
KOS
Knowledge Organization System (KOS) refers to a structured framework for organizing, managing, and retrieving information within a specific domain or across multiple domains. Essential for improving information findability, enhancing semantic interoperability, and supporting effective knowledge management in digital environments.
IoT
Internet of Things (IoT) refers to a network of interconnected physical devices embedded with electronics, software, sensors, and network connectivity, enabling them to collect and exchange data. Essential for creating smart, responsive environments and improving efficiency across various industries by enabling real-time monitoring, analysis, and automation.
Sentiment Analysis
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.
PAR
Performance and Accountability Reporting (PAR) is a comprehensive document that outlines an organization's performance in achieving its goals and its accountability in managing resources. This report is essential for transparency, governance, and continuous improvement.
CBR
Case-Based Reasoning (CBR) is an AI method that solves new problems based on the solutions of similar past problems. This approach is essential for developing intelligent systems that learn from past experiences to improve problem-solving capabilities.
Demand Forecasting
The process of predicting future customer demand using historical data and other information. Crucial for optimizing inventory levels, production schedules, and supply chain management.