Confirming Subscription
A dark pattern where users are tricked into confirming a subscription through misleading language or design. It's crucial to avoid misleading users and ensure clear communication about subscription terms and conditions.
18 topics found for:
“misleading design”
Bait and Switch
A dark pattern where users think they are going to take one action, but a different, undesirable action happens instead. Designers must avoid this deceptive practice and be aware of its impact to ensure transparent user interactions.
False Bottom
A design flaw where users mistakenly believe they have reached the end of the content due to a misleading visual cue. Crucial for ensuring content is properly signposted to avoid user confusion and ensure thorough exploration.
Subscription Trap
A dark pattern where it's easy to subscribe but very difficult to cancel the subscription. Awareness of this tactic is important to provide straightforward and user-friendly subscription management.
Sneak Peek
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.
Disguised Ads
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.
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.
Hidden Subscription
A dark pattern where users are unknowingly signed up for a recurring subscription. Awareness of this tactic is important to ensure transparent subscription services and prevent deceptive charges.
Trick Questions
A dark pattern where questions are worded in a way that tricks the user into giving an answer they didn't intend. Recognizing the harm of this practice is important to maintain clarity and honesty in user interactions.
Click Bait
Content designed to attract clicks by using sensational or misleading headlines. Important for recognizing and avoiding practices that can harm user trust and content quality.
Berkson’s Paradox
A statistical phenomenon where two independent events appear to be correlated due to a selection bias. Important for accurately interpreting data and avoiding misleading conclusions.
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.
Numerosity Bias
The tendency to perceive a greater quantity as a better value, regardless of the actual utility. Important for understanding consumer behavior and designing effective marketing strategies.
Anecdotal Fallacy
A logical fallacy where anecdotal evidence is used to make a broad generalization. Crucial for improving critical thinking and avoiding misleading conclusions.
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.
Vanity Metrics
Metrics that may look impressive but do not provide meaningful insights into the success or performance of a product or business, such as total page views or social media likes. Important for distinguishing between metrics that drive real business value and those that do not.
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.
Pseudocertainty Effect
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.