Error Prevention
Design strategies aimed at preventing user errors before they occur. Crucial for enhancing usability and ensuring a smooth user experience.
16 topics found for:
“error prevention”
Poka-Yoke Principle
A Japanese term for "mistake-proofing," referring to any mechanism or process that helps prevent errors by design. Crucial for designing systems and processes that minimize the risk of human error.
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.
Anomaly Detection
The process of identifying unusual patterns or outliers in data that do not conform to expected behavior. Crucial for detecting fraud, errors, or other significant deviations in various contexts.
IDE
Integrated Development Environment (IDE) is a software suite that combines tools like code editors, debuggers, and compilers. Essential for improving developer productivity and ensuring efficient and error-free coding practices.
Anecdotal Fallacy
A logical fallacy where anecdotal evidence is used to make a broad generalization. Crucial for improving critical thinking and avoiding misleading conclusions.
Muri
A Japanese word meaning excessive strain on people or processes. Crucial for preventing burnout and maintaining sustainable work practices.
Machine Learning Bias
Systematic errors in AI models that arise from the data or algorithms used, leading to poor outcomes. Important for ensuring fairness and accuracy in AI systems.
Fallacy of Composition
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.
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.
Survivorship Bias
A cognitive bias that occurs when conclusions are drawn from a non-representative sample, focusing only on successful cases and ignoring failures. Crucial for making accurate assessments and designing systems that consider both successes and failures.
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.
Destructive Action
An action in a user interface that, once performed, cannot be undone and typically involves deleting or removing content. Important for emphasizing the severity of the action and ensuring user confirmation to prevent accidental data loss.
Nielsen’s Heuristics
A set of ten general principles for user interface design created by Jakob Nielsen to improve usability. Essential for evaluating and improving user interface designs.
Test Environment
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.
POLA
Principle of Least Astonishment (POLA) is a design guideline stating that interfaces should behave in a way that users expect to avoid confusion. Crucial for enhancing user experience and reducing the learning curve in digital products.