Error Prevention
Design strategies aimed at preventing user errors before they occur. Crucial for enhancing usability and ensuring a smooth user experience.
Design strategies aimed at preventing user errors before they occur. Crucial for enhancing usability and ensuring a smooth user experience.
A theory that describes how individuals pursue goals using either a promotion focus (seeking gains) or a prevention focus (avoiding losses). Crucial for designing motivation strategies and understanding user behavior in goal pursuit.
A tool in Google Search Console that allows webmasters to instruct Google to ignore certain backlinks, typically used to combat negative SEO. Crucial for maintaining a healthy backlink profile and protecting against negative SEO practices.
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.
A risk management model that illustrates how multiple layers of defense (like slices of Swiss cheese) can prevent failures, despite each layer having its own weaknesses. Crucial for understanding and mitigating risks in complex systems.
A strategy used to determine the proportion of various SMEs needed to support a pipeline of work. Important for optimizing resource allocation, enhancing efficiency, and ensuring teams have the appropriate support based on design demand and complexity.
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.
The percentage of times a keyword appears in a text relative to the total number of words, used to evaluate the relevance and optimization of a webpage for specific search terms. Important for optimizing content for search engines without overstuffing keywords.
The practice of protecting systems, networks, and programs from digital attacks, unauthorized access, and data breaches. Essential for safeguarding sensitive information, maintaining user trust, and ensuring the integrity and functionality of digital products and services.
Obstacles to effective communication that arise from differences in understanding the meanings of words and symbols used by the communicators. Crucial for designing clear and effective communication systems and avoiding misunderstandings.
The preferred version of a web page that search engines should index, used to avoid duplicate content issues and improve SEO. Essential for managing SEO and ensuring the correct indexing of web pages.
A logical fallacy where anecdotal evidence is used to make a broad generalization. Crucial for improving critical thinking and avoiding misleading conclusions.
Interference in the communication process caused by ambiguity in the meaning of words and phrases, leading to misunderstandings. Crucial for designing clear communication channels and reducing misunderstandings in user interactions.
Responsive Web Design (RWD) is an approach to web design that makes web pages render well on a variety of devices and window or screen sizes. Essential for creating flexible, adaptive web experiences that maintain functionality and aesthetics across different platforms and devices.
A dark pattern where practices are used to make it hard for users to compare prices with other options. It's essential to avoid this tactic and promote fair competition by allowing users to make informed decisions.
A cognitive bias where people underestimate the complexity and challenges involved in scaling systems, processes, or businesses. Important for understanding the difficulties of scaling and designing systems that address these challenges.
A psychological phenomenon where the desire for harmony and conformity in a group results in irrational or dysfunctional decision-making. Crucial for recognizing and mitigating the risks of poor decision-making in teams.
A reading pattern where users skip over certain sections of content, often due to a lack of perceived relevance. Crucial for designing content that is engaging and relevant to prevent users from bypassing important information.
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.
A dark pattern where users are shown a preview of content that is then gated behind a paywall or sign-up. It's crucial to avoid this misleading practice and be transparent about access requirements.
Also known as feature creep, the continuous addition of new features to a product, often beyond the original scope, leading to project delays and resource strain. Important for managing project scope and ensuring timely delivery.
The phenomenon where having too many options leads to anxiety and difficulty making a decision, reducing overall satisfaction. Important for designing user experiences that balance choice and simplicity to enhance satisfaction.
Actions, messages, or visuals that do not align with the established brand identity and values. Important for identifying and correcting deviations from brand standards.
A dark pattern where advertisements are disguised as other types of content or navigation to trick users into clicking on them. Awareness of this tactic is crucial to maintain transparency and prevent misleading users with disguised content.
The pursuit of a healthy relationship with technology, balancing its use to enhance well-being without causing harm. Important for promoting healthy technology use and designing user experiences that support well-being.
A symbol, word, or words legally registered or established by use as representing a company or product. Crucial for protecting brand identity and ensuring legal rights to brand elements.
The phenomenon where having too many options leads to decision-making paralysis and decreased satisfaction. Crucial for understanding and designing user interfaces that avoid overwhelming users with choices.
A term used to describe an organization focused on continuously shipping new features, often at the expense of quality, user experience, or business value. Crucial for recognizing and addressing the pitfalls of prioritizing quantity over quality in feature development.
The risk that the product cannot be built as envisioned due to technical limitations, resource constraints, or other practical challenges. Important for confirming that the product can be realistically developed and deployed with the available technology and resources.
A cognitive bias where individuals or organizations continue to invest in a failing project or decision due to the amount of resources already committed. Important for designers to recognize and mitigate their own risks of continuing unsuccessful initiatives.
A cognitive bias where people seek out more information than is needed to make a decision, often leading to analysis paralysis. Crucial for designing decision-making processes that avoid information overload for users.
A cognitive bias where people perceive past events as having been more predictable than they actually were. Important for understanding and mitigating biases in user feedback and decision-making.
A phenomenon where users fail to notice significant changes in their visual field. Important for understanding and designing around potential user perception issues.
A method of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.
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.
A product development methodology that emphasizes shaping work before starting it, fixing time and team size but leaving scope flexible to ensure high-quality outcomes. Crucial for managing product development efficiently and delivering high-quality results within constraints.
Mutually Exclusive, Collectively Exhaustive (MECE) is a problem-solving framework ensuring that categories are mutually exclusive and collectively exhaustive, avoiding overlaps and gaps. Essential for structured thinking and comprehensive analysis in problem-solving.
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.