A cognitive bias where individuals underestimate their own abilities and performance relative to others, believing they are worse than average. Important for understanding self-perception biases among designers and designing systems that support accurate self-assessment.
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.
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 cognitive bias where individuals with low ability at a task overestimate their ability, while experts underestimate their competence. Crucial for designers to create educational content and user interfaces that accommodate varying levels of user expertise.
A cognitive bias where individuals overestimate their own abilities, qualities, or performance relative to others. Important for understanding user self-perception and designing systems that account for inflated self-assessments.
The practicality of implementing a solution based on technical constraints and capabilities. Crucial for evaluating the viability of design and development projects.
A statistical technique that uses random sampling and statistical modeling to estimate mathematical functions and simulate systems. Useful for risk assessment, decision-making, and performance optimization in digital product design.
The potential for a project or solution to be economically sustainable and profitable. Important for ensuring that design and development efforts align with business goals and market demands.
A thorough examination of a brand's current position in the market and its effectiveness in reaching its goals. Important for assessing brand health and identifying areas for improvement.
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.
An analysis that assesses the practicality and potential success of a proposed project or system. Crucial for determining the viability and planning of new initiatives.
A cognitive bias where individuals underestimate the time, costs, and risks of future actions while overestimating the benefits. Important for realistic project planning and setting achievable goals for designers.
The process of identifying and assessing the influence and interest of various stakeholders in a project, to prioritize engagement and communication strategies. Crucial for effectively managing stakeholder relationships and ensuring project success.
A cognitive bias where people perceive an outcome as certain while it is actually uncertain, based on how information is presented. Crucial for understanding and mitigating biased user decision-making.
An analysis comparing the costs and benefits of a decision or project to determine its feasibility and value. Important for making informed business and design decisions.
The process of evaluating a product by testing it with real users to gather feedback and identify usability issues. Essential for validating design decisions and ensuring the product meets user needs.
The process of collecting and documenting the needs and expectations of stakeholders for a new or modified product or system. Essential for ensuring that the final product meets user needs and business objectives.
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 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 determining whether there is a need or demand for a product in the target market, often through testing and feedback. Crucial for ensuring that a product will meet market needs and be successful.
A systematic process for determining and addressing needs or gaps between current conditions and desired outcomes. Important for identifying user requirements and guiding the development of digital products that meet those needs.
The tendency to believe that large or significant events must have large or significant causes. Important for understanding cognitive biases in decision-making and designing systems that present accurate causal relationships.
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.
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.
The principle that the more a metric is used to make decisions, the more it will be subject to corruption and distort the processes it is intended to monitor. Important for understanding the limitations and potential distortions of metrics in design and evaluation.
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.
An experimental design where different groups of participants are exposed to different conditions, allowing for comparison between groups. Important for understanding and applying different experimental designs in user research.
A prioritization framework used to assess and compare the value a feature will deliver to users against the complexity and cost of implementing it. Crucial for making informed decisions about feature prioritization and resource allocation.
The risk that users will find the product difficult or confusing to use, preventing them from effectively utilizing its features. Crucial for making sure the product is user-friendly and intuitive, enhancing the user experience and adoption.
A relative estimation technique used in Agile project management to quickly assess the size and complexity of tasks by assigning them T-shirt sizes (e.g., small, medium, large). Crucial for efficient project planning and workload management.
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.
Also known as the 68-95-99.7 Rule, it states that for a normal distribution, nearly all data will fall within three standard deviations of the mean. Important for understanding the distribution of data and making predictions about data behavior in digital product design.
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.
A cognitive bias where people wrongly believe they have direct insight into the origins of their mental states, while treating others' introspections as unreliable. Important for designing experiences that account for discrepancies between user self-perception and actual behavior.
Total Addressable Market (TAM) represents the total revenue opportunity available if a product or service achieves 100% market share. Essential for understanding the full potential of a market.
Serviceable Obtainable Market (SOM) is the portion of the Serviceable Addressable Market that a company can realistically capture. Essential for setting achievable sales and market share goals.
A cognitive bias where individuals overestimate the likelihood of extreme events regressing to the mean. Crucial for understanding decision-making and judgment under uncertainty.
The degree to which a product satisfies strong market demand, often considered a key indicator of a product's potential for success. Essential for validating the viability of a product in the market and guiding strategic decisions.
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.
A cognitive bias where people overestimate the probability of success for difficult tasks and underestimate it for easy tasks. Useful for designers to understand user confidence and design
A cognitive bias where people overestimate the importance of information that is readily available. Essential for designers to understand and mitigate how easily accessible information can disproportionately influence decisions.
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.
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.
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.
A metric that predicts how well a website will rank on search engine result pages (SERPs), based on factors like backlink quality and quantity. Important for understanding and improving a website's search engine performance.
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.
Lifetime Value (LTV) is a metric that estimates the total revenue a business can expect from a single customer account throughout their relationship. Crucial for informing customer acquisition strategies, retention efforts, and overall business planning by providing insights into long-term customer profitability.