McNamara Fallacy
The error of making decisions based solely on quantitative observations and ignoring all other factors. Important for ensuring a holistic approach to decision-making.
26 topics found for:
“decision-making errors”
Focusing Effect
A cognitive bias where people place too much importance on one aspect of an event, causing errors in judgment. Important for understanding decision-making and designing interfaces that provide balanced information.
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.
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.
Irrational Escalation
The phenomenon where people continue a failing course of action due to the amount of resources already invested. Important for recognizing and mitigating biased decision-making.
Conjunction Fallacy
A logical fallacy where people assume that specific conditions are more probable than a single general one. Important for understanding and addressing cognitive biases in user behavior.
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.
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.
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.
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.
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.
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.
BRE
Business Rules Engine (BRE) is a software system that executes one or more business rules in a runtime production environment. Crucial for automating decision-making processes and ensuring consistency and compliance in digital products.
HITL
Human in the Loop (HITL) integrates human judgment into the decision-making process of AI systems. Crucial for ensuring AI reliability and alignment with human values.
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.
Gambler’s Fallacy
A cognitive bias where individuals believe that past random events affect the probabilities of future random events. Important for designers to understand user decision-making biases related to randomness.
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.
XAI
Explainable AI (XAI) are AI systems that provide clear and understandable explanations for their decisions and actions. This transparency is crucial for building trust and confidence in AI applications across various domains.
Introspection Illusion
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.
Anecdotal Fallacy
A logical fallacy where anecdotal evidence is used to make a broad generalization. Crucial for improving critical thinking and avoiding misleading conclusions.
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.
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.
Group Attribution Error
A cognitive bias where people attribute group behavior to the characteristics of the group members rather than the situation. Crucial for understanding team dynamics and avoiding misattribution in collaborative settings.
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.
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.
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.